{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle house price prediction with ``Gluon`` and k-fold cross-validation\n",
    "\n",
    "**Updates: [Share your score and method here](https://discuss.mxnet.io/t/kaggle-exercise-1-house-price-prediction-with-gluon/51).**\n",
    "\n",
    "How have you been doing so far on the journey of  `Deep Learning---the Straight Dope`?\n",
    "\n",
    "> It's time to get your hands dirty. \n",
    "\n",
    "Let's get started.\n",
    "\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this tutorial, we introduce how to use ``Gluon`` for competition on [Kaggle](https://www.kaggle.com). Specifically, we will take the [house price prediction problem](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) as a case study. \n",
    "\n",
    "We will provide a basic end-to-end pipeline for completing a common Kaggle challenge. We will demonstrate how to use ``pandas`` to pre-process the real-world data sets, such as:\n",
    "\n",
    "* Handling categorical data\n",
    "* Handling missing values\n",
    "* Standardizing numerical features\n",
    "\n",
    "Note that this tutorial only provides a very basic pipeline. We expect you to tweak the following code, such as re-designing models and fine-tuning parameters, to obtain a desirable score on Kaggle.\n",
    "\n",
    "\n",
    "## House Price Prediction Problem on Kaggle\n",
    "\n",
    "[Kaggle](https://www.kaggle.com) is a popular platform for people who love machine learning. To submit the results, please register a [Kaggle](https://www.kaggle.com) account. Please note that, **Kaggle limits the number of daily submissions to 10**.\n",
    "\n",
    "\n",
    "![](../img/kaggle.png)\n",
    "\n",
    "\n",
    "We take the [house price prediction problem](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) as an example to demonstrate how to complete a Kaggle competition with ``Gluon``. Please learn details of the problem by clicking the [link to the house price prediction problem](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) before starting.\n",
    "\n",
    "![](../img/house_pricing.png)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Load the data set\n",
    "\n",
    "There are separate training and testing data sets for this competition. Both data sets describe features of every house, such as type of the street, year of building, and basement conditions. Such features can be numeric, categorical, or even missing (`na`). Only the training dat set has the sale price of each house, which shall be predicted based on features of the testing data set.\n",
    "\n",
    "The data sets can be downloaded via the [link to problem](https://www.kaggle.com/c/house-prices-advanced-regression-techniques). Specifically, you can directly access the [training data set](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/download/train.csv) and the [testing data set](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/download/test.csv) after logging in Kaggle.\n",
    "\n",
    "We load the data via `pandas`. Please make sure that it is installed (``pip install pandas``)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "train = pd.read_csv(\"../data/kaggle/house_pred_train.csv\")\n",
    "test = pd.read_csv(\"../data/kaggle/house_pred_test.csv\")\n",
    "all_X = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'],\n",
    "                      test.loc[:, 'MSSubClass':'SaleCondition']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a look at a few rows of the training data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the shapes of the data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing data\n",
    "\n",
    "We can use ``pandas`` to standardize the numerical features:\n",
    "\n",
    "$$x_i = \\frac{x_i - \\mathbb{E} x_i}{\\text{std}(x_i)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_feas = all_X.dtypes[all_X.dtypes != \"object\"].index\n",
    "all_X[numeric_feas] = all_X[numeric_feas].apply(\n",
    "    lambda x: (x - x.mean()) / (x.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us transform categorical values to numerical ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_X = pd.get_dummies(all_X, dummy_na=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can approximate the missing values by the mean values of the current feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_X = all_X.fillna(all_X.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us convert the formats of the data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_train = train.shape[0]\n",
    "\n",
    "X_train = all_X[:num_train].as_matrix()\n",
    "X_test = all_X[num_train:].as_matrix()\n",
    "y_train = train.SalePrice.as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data in `NDArray`\n",
    "\n",
    "To facilitate the interations with ``Gluon``, we need to load data in the `NDArray` format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mxnet import ndarray as nd\n",
    "from mxnet import autograd\n",
    "from mxnet import gluon\n",
    "\n",
    "X_train = nd.array(X_train)\n",
    "y_train = nd.array(y_train)\n",
    "y_train.reshape((num_train, 1))\n",
    "\n",
    "X_test = nd.array(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the loss function to be the squared loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "square_loss = gluon.loss.L2Loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below defines the root mean square loss between the logarithm of the predicted values and the true values used in the competition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rmse_log(net, X_train, y_train):\n",
    "    num_train = X_train.shape[0]\n",
    "    clipped_preds = nd.clip(net(X_train), 1, float('inf'))\n",
    "    return np.sqrt(2 * nd.sum(square_loss(\n",
    "        nd.log(clipped_preds), nd.log(y_train))).asscalar() / num_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the model\n",
    "\n",
    "We define a **basic** linear regression model here. This may be modified to achieve better results on Kaggle. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_net():\n",
    "    net = gluon.nn.Sequential()\n",
    "    with net.name_scope():\n",
    "        net.add(gluon.nn.Dense(1))\n",
    "    net.initialize()\n",
    "    return net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the training function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def train(net, X_train, y_train, X_test, y_test, epochs, \n",
    "          verbose_epoch, learning_rate, weight_decay):\n",
    "    train_loss = []\n",
    "    if X_test is not None:\n",
    "        test_loss = []\n",
    "    batch_size = 100\n",
    "    dataset_train = gluon.data.ArrayDataset(X_train, y_train)\n",
    "    data_iter_train = gluon.data.DataLoader(\n",
    "        dataset_train, batch_size,shuffle=True)\n",
    "    trainer = gluon.Trainer(net.collect_params(), 'adam',\n",
    "                            {'learning_rate': learning_rate,\n",
    "                             'wd': weight_decay})\n",
    "    net.collect_params().initialize(force_reinit=True)\n",
    "    for epoch in range(epochs):\n",
    "        for data, label in data_iter_train:\n",
    "            with autograd.record():\n",
    "                output = net(data)\n",
    "                loss = square_loss(output, label)\n",
    "            loss.backward()\n",
    "            trainer.step(batch_size)\n",
    "\n",
    "            cur_train_loss = get_rmse_log(net, X_train, y_train)\n",
    "        if epoch > verbose_epoch:\n",
    "            print(\"Epoch %d, train loss: %f\" % (epoch, cur_train_loss))\n",
    "        train_loss.append(cur_train_loss)\n",
    "        if X_test is not None:    \n",
    "            cur_test_loss = get_rmse_log(net, X_test, y_test)\n",
    "            test_loss.append(cur_test_loss)\n",
    "    plt.plot(train_loss)\n",
    "    plt.legend(['train'])\n",
    "    if X_test is not None:\n",
    "        plt.plot(test_loss)\n",
    "        plt.legend(['train','test'])\n",
    "    plt.show()\n",
    "    if X_test is not None:\n",
    "        return cur_train_loss, cur_test_loss\n",
    "    else:\n",
    "        return cur_train_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Fold Cross-Validation\n",
    "\n",
    "We described the [overfitting problem](../chapter02_supervised-learning/regularization-scratch.ipynb), where we cannot rely on the training loss to infer the testing loss. In fact, when we fine-tune the parameters, we typically rely on $k$-fold cross-validation.\n",
    "\n",
    "> In $k$-fold cross-validation, we divide the training data sets into $k$ subsets, where one set is used for the validation and the remaining $k-1$ subsets are used for training.\n",
    "\n",
    "We care about the average training loss and average testing loss of the $k$ experimental results. Hence, we can define the $k$-fold cross-validation as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,\n",
    "                       learning_rate, weight_decay):\n",
    "    assert k > 1\n",
    "    fold_size = X_train.shape[0] // k\n",
    "    train_loss_sum = 0.0\n",
    "    test_loss_sum = 0.0\n",
    "    for test_i in range(k):\n",
    "        X_val_test = X_train[test_i * fold_size: (test_i + 1) * fold_size, :]\n",
    "        y_val_test = y_train[test_i * fold_size: (test_i + 1) * fold_size]\n",
    "\n",
    "        val_train_defined = False\n",
    "        for i in range(k):\n",
    "            if i != test_i:\n",
    "                X_cur_fold = X_train[i * fold_size: (i + 1) * fold_size, :]\n",
    "                y_cur_fold = y_train[i * fold_size: (i + 1) * fold_size]\n",
    "                if not val_train_defined:\n",
    "                    X_val_train = X_cur_fold\n",
    "                    y_val_train = y_cur_fold\n",
    "                    val_train_defined = True\n",
    "                else:\n",
    "                    X_val_train = nd.concat(X_val_train, X_cur_fold, dim=0)\n",
    "                    y_val_train = nd.concat(y_val_train, y_cur_fold, dim=0)\n",
    "        net = get_net()\n",
    "        train_loss, test_loss = train(\n",
    "            net, X_val_train, y_val_train, X_val_test, y_val_test, \n",
    "            epochs, verbose_epoch, learning_rate, weight_decay)\n",
    "        train_loss_sum += train_loss\n",
    "        print(\"Test loss: %f\" % test_loss)\n",
    "        test_loss_sum += test_loss\n",
    "    return train_loss_sum / k, test_loss_sum / k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and cross-validate the model\n",
    "\n",
    "The following parameters can be fine-tuned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 5\n",
    "epochs = 100\n",
    "verbose_epoch = 95\n",
    "learning_rate = 5\n",
    "weight_decay = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the above parameters, we can train and cross-validate our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96, train loss: 0.201713\n",
      "Epoch 97, train loss: 0.199597\n",
      "Epoch 98, train loss: 0.197594\n",
      "Epoch 99, train loss: 0.195709\n"
     ]
    },
    {
     "data": {
      "image/png": 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5hL4n1wf4JaHbpneGmSfqmRlJvcdQMOtfxFsRO2f8g9QOx3odS0REROqYcMtb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd96f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.188469\n",
      "Epoch 96, train loss: 0.198065\n",
      "Epoch 97, train loss: 0.195917\n",
      "Epoch 98, train loss: 0.193840\n",
      "Epoch 99, train loss: 0.191888\n"
     ]
    },
    {
     "data": {
      "image/png": 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tHicREREBn89HIBBQgfOYc45AIFCrS22pvIVJS2SJiEh9Eh8fTyAQYPPmzSpwHikpKWHD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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112204630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.211287\n",
      "Epoch 96, train loss: 0.199244\n",
      "Epoch 97, train loss: 0.197074\n",
      "Epoch 98, train loss: 0.194988\n",
      "Epoch 99, train loss: 0.193050\n"
     ]
    },
    {
     "data": {
      "image/png": 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TUXyovKUEtESWiIh4z+fzEQgEVOA85pwjEAjU6FJbKm9hCiRmAJDidkBp0X6OFhERqVnx\n8fEEAgE2bNigAueR0tJS1q5dSyAQIDk5uca+RysshCup+e6XpVvXE5Pe1sMwIiLS0LVo0YKioiJy\nc3PJz8/H7/dHzULr0c45RzAY3L1+amJiIk2aNKmx71N5C5M/LXP3662bV5Ou8iYiIh7y+Xy0bduW\n9evXU1RURDAY9DpSg2FmxMTE0KhRI1JTU0lJSanR4qzyFqb4xr+Ut+2b1pLexcMwIiIihApcVlaW\n1zGkhumetzAll18iK2+th0lERESkIQmrvJlZLzN70cyWmlmBmW0ys8/N7JRKjB1vZu43tsz9ja8r\n0pr9Ut60RJaIiIjUlnAvm7YDUoAngTVAIjAaeN3MLnTOPVKJz7gZWLbXvrww89S6po3T2OoakWo7\ncdtU3kRERKR2hFXenHNvA2+X32dmDwA/AFcClSlv7zjnZoTz/XVBcnwMOaSRyk4tkSUiIiK1JmL3\nvDnnAsBKoHFlx5hZipn5I5WhNmmJLBEREfFCtZ42NbMkoBGQBowATgBeqOTwT4BkoNjM3gOucs4t\nrsR3Ngea7bW7U6VDR9CW2Ewomk968Wovvl5EREQaoOpOFXI3cGHZ6yDwMjBxP2MKgCcIlbetwABC\nl1qnm1l/59zK/YyfANwSbuBIyk/qAEWfkB7YBEXbID7F60giIiJSz1W3vN0HTANaAuMAPxC3rwHO\nuanA1HK7Xi078/Y5cANw0X6+8yHgxb32dQJeq3zsCMnoCrmhl8XrFxLX9qBajyAiIiINS7XKm3Nu\nAbCg7MenzOx94A0zG+SqsLCac+5LM/sWOKYSx24ANpTf59XyH0mtesCi0OvNy+eSpfImIiIiNSzS\nk/ROAwYCXcMYuxJIj2ycmtWifQ8CLlQcd6ye73EaERERaQgiXd4alf2ZFsbYjkBUzbnRvkU6K13Z\nAvWbFnkbRkRERBqEcFdYaF7Bvljgd8BOYF7Zviwz61723q7j9n5SFDM7kdCDC++Gk8crKQmxrPK3\nBiBp297zDYuIiIhEXrj3vP2fmaUSeshgNZAJnAV0JzTlx/ay4+4AzgU6ADll+6ab2Y/ADCAf6A+c\nR+iy6e1h5vFMXlJ72P4DGUUrIRgAX1ROWyciIiJRItzy9gLwB+BioCmwjdDqCn92zr1eibEnAccS\nWlZrLfAoMMk5F3XrTJU26QzbIZZS3JblWNOOXkcSERGReizc5bGeB56vxHHjgfF77bsRuDGc762L\n4lp0D50zBPJWZNNE5U1ERERqUKQfWGhwmrTrtft1/spsD5OIiIhIQ6DyVk3t2rQl1yUDULJ+ocdp\nREREpL5TeaumzNQEltEKgLi8JR6nERERkfpO5a2afD5jU0I7AJrszPE2jIiIiNR7Km8RsDM19JBC\najAfdmz2OI2IiIjUZypvEeDL+GU1sJ3rFuzjSBEREZHqUXmLgJQ2PXa/zs2Z62ESERERqe9U3iIg\ns113il1oZYWCtVqgXkRERGqOylsEdGieRo7LBMC3ebHHaURERKQ+U3mLgIRYP2ti2wCQsl0L1IuI\niEjNUXmLkG1JHQBoWrIWSos8TiMiIiL1lcpbhASadgHAT5DA5qUepxEREZH6SuUtQhIyf3nidMty\nPXEqIiIiNUPlLUIy2vXc/XrrqnkeJhEREZH6TOUtQtq3zmKdawJAYIMWqBcREZGaofIWIU2T4lhu\noQXqE/J1z5uIiIjUDJW3CDEzchuFFqhvWrgcnPM4kYiIiNRHKm8RVJTWCYBEVwDb1nmcRkREROoj\nlbcI8jfvtvv19tV6aEFEREQiT+UtgtLa/PLEae7ybA+TiIiISH2l8hZBrdp1psDFA1C0TgvUi4iI\nSOSpvEVQ26bJLHNZAMTk/uxxGhEREamPVN4iKNbvY11cWwAa79B0ISIiIhJ5Km8RlpcaemihSWAT\nbF3jcRoRERGpb1TeIqy05UG7Xxcs+9bDJCIiIlIfqbxFWGb3QylxfgByF3zpcRoRERGpb1TeIqxv\nhyzmu9B9b7Z6hsdpREREpL4Jq7yZWS8ze9HMlppZgZltMrPPzeyUSo5vbGaPmNlGM9thZp+YWf9w\nstQ1TZLiWBrfA4Bm2+ZBoMTjRCIiIlKfhHvmrR2QAjwJXA7cVrb/dTO7YF8DzcwHvAWcCTwAXAs0\nBz41sy5h5qlTdjTrB0CcK8atm+NxGhEREalPYsIZ5Jx7G3i7/D4zewD4AbgSeGQfw8cAg4Gxzrlp\nZWOnAouASYRKXVRL7DwYyh403bTgK5q1qhcnFUVERKQOiNg9b865ALASaLyfQ8cA64GXy43dCEwF\nTjWz+Ehl8krXbr3Z7FIAKFj6jcdpREREpD6pVnkzsyQzyzCzTmb2J+AE4KP9DOsHzHTOBffa/x2Q\nCHTdz3c2L7vnbvcGdAr3d6gJ3TJT+YnQFeDkjTM9TiMiIiL1SViXTcu5G7iw7HWQ0Nm0ifsZkwV8\nXsH+tWV/tgT2daPYBOCWKmSsdTF+H+tT+8K2mTQtXgPbN0JyM69jiYiISD1Q3cum9wHDgXOBdwA/\nELefMY2Aogr2F5Z7f18eAnrvtZ1ayby1xrUeuPt1YY4m6xUREZHIqFZ5c84tcM596Jx7yjl3MpAM\nvGFmto9hO4GK7mtLKPf+vr5zg3Muu/wGLAnrF6hBzbsdQsCF/jFs0mS9IiIiEiGRnqR3GjCQfd+3\ntpbQpdO97dpXLxYE7dupDYtcm9APq773NoyIiIjUG5Eub7sueabt45hZQP+y+d7KGwQUEJoyJOo1\nS4lnUVx3ADLy50Iw4HEiERERqQ/CXWGheQX7YoHfEbrsOa9sX5aZdS97b5dpQAtgVLmxGcBY4A3n\nXEX3w0Wl7RmhyXoTXCFufbbHaURERKQ+CPdp0/8zs1RCT42uBjKBs4DuwFXOue1lx91B6GGGDkBO\n2b5pwDfA42bWE9hE6AlSP3X8KdKqSux4CKwLvd6yaDrpWX29DSQiIiJRL9zLpi8QmhrkYuDfhFZV\nWAWc6py7Z18DyybzPbHsMy4D/kGowB3lnFsYZp46qWP3A8l3iQBsX/K1x2lERESkPgh3eazngecr\ncdx4YHwF+7cA55dt9VaPlo35xnVhqM2m0XpN1isiIiLVF+kHFqScuBgfa1P6ANCsaAUU5HqcSERE\nRKKdylsNC7Y6aPfr4hWaMkRERESqR+WthmV0G7z79cb5mqxXREREqkflrYb16dyOxcFWAARXfudx\nGhEREYl2Km81LDMtgQUxocl6m+bN0WS9IiIiUi0qb7UgL2MAAInBHbjVeupUREREwqfyVgtiuh2z\n+/WWn972MImIiIhEO5W3WjCwT0/mB9sCULrwfY/TiIiISDRTeasFnZolMyM2dOk0Y2s27NjkcSIR\nERGJVipvtcDMKGx/FAA+HMULP/Q4kYiIiEQrlbda0v7AI9nqGgGQO/stj9OIiIhItFJ5qyWHds3i\naxdaKitl9ecQDHqcSERERKKRylstSY6PYXl6aLWFpNI83JofPU4kIiIi0UjlrRYl9Txu92tNGSIi\nIiLhUHmrRQMP6MP8YBtAU4aIiIhIeFTealGX5sn8sGvKkPw5UJDrcSIRERGJNipvtcjM2NlOU4aI\niIhI+FTealnbA49ku0sANGWIiIiIVJ3KWy0b3DWL6a43ACmrPtWUISIiIlIlKm+1LCUhluVNfpky\nhLWzPE4kIiIi0UTlzQMJPY/f/VpThoiIiEhVqLx5YOABfVgYbA1A8QJNGSIiIiKVp/LmgW4tUpgR\n0x+AZpoyRERERKpA5c0DZsaOdscA4CNIydxXPU4kIiIi0ULlzSNt+x/DOtcEgK3fT/E4jYiIiEQL\nlTePDOueyXuEnjptsvF72LrG40QiIiISDVTePJIQ62dzxxFAaLWFwlkvepxIREREokFY5c3MBprZ\nA2aWbWY7zGyFmU01s66VGDvezNxvbJnh5IlWAw45imXBFgAU/PCCx2lEREQkGsSEOe7PwGHAi8BP\nQCYwEZhpZoc45+ZW4jNuBpbttS8vzDxR6bDOGfzXP5QL3Iuk52fD5iXQtJPXsURERKQOC7e83QOc\n6Zwr3rXDzF4A5gDXAWdX4jPecc7NCPP764UYv4/C7iNhfuiS6Y4fnifp2Bs8TiUiIiJ1WViXTZ1z\n08sXt7J9i4FsoEdlP8fMUszMH06G+mLwoEOZG2wPQOmsqeCct4FERESkTovYAwtmZkALYFMlh3wC\nbAUKzOx1M+tSye9pbma9ym9A1F5r7N+2CZ/FDQUgrSAH1v3kbSARERGp0yL5tOlZQCtgf3feFwBP\nAJcAI4E7gaOB6WbWphLfMwGYu9f2WniRvefzGfQes/tnzfkmIiIi+xKR8mZm3YEHga+BJ/d1rHNu\nqnPu9865p5xzrzrnbgKOA5oClbnh6yGg917bqdXJ77WjBvXj22B3AHzZL0Mw6HEiERERqauqXd7K\npvd4C8gHxjjnAlX9DOfcl8C3wDGVOHaDcy67/AYsqep31iXdM1P4JvFIAJKL1sOKrz1OJCIiInVV\ntcqbmaUB7wCNgeOdc9VZJmAlkF6dPNHKzEjqN5oSF3p2I+87XToVERGRioVd3swsAXgD6Aqc7Jyb\nV80sHYGN1fyMqDX8oJ58EewDQNyi1yFQ4nEiERERqYvCXWHBT+jBhEOBsc65Cq/zmVmWmXU3s9hy\n+5pVcNyJwADg3XDy1AftmiYxu3HoqnFiaT5uUYP9RyEiIiL7EO4kvXcDIwideUs3sz0m5XXOPVP2\n8g7gXKADkFO2b7qZ/QjMIHSfXH/gPEKXTW8PM0+9kDFwNFs/epBU28nWLx4mrccpXkcSERGROibc\n8nZg2Z+nlG17e6aCfbu8AJwEHAskAmuBR4FJzrn1YeapF47v14lXPjiCc/3vkrbmS9j0M2R09jqW\niIiI1CHhrrAwzDlnv7WVO2582b6ccvtudM71c841ds7FOefaOecmNPTiBtAsJZ6Vnc7Y/fPOrx/x\nMI2IiIjURZGcpFci4NgjhvJloBcAvlnPQvEOjxOJiIhIXaLyVscMbN+ET1JGABAf2E7wp2keJxIR\nEZG6ROWtjjEzOh4+ljUuNOXd9i//rcXqRUREZDeVtzrotAHtmMZwAFLz5sOq7z1OJCIiInWFylsd\nlBQfQ1GfsyguW3Fh+xf/9jiRiIiI1BUqb3XUyKH9eTd4MAAJi9+A7Q128QkREREpR+WtjurcPIWZ\nzUcDEONKKJnxpMeJREREpC5QeavDBh1xEvODbQAo/vYxCAY8TiQiIiJeU3mrw47plclrsScAkLRz\nLW7BWx4nEhEREa+pvNVhsX4fqQefTb5LBKDg47s0bYiIiEgDp/JWx405tBtPB48HIGnTbFjysceJ\nRERExEsqb3Vc89QENvc6jx0uHoCCj/7ucSIRERHxkspbFDj3mP48GzgGgMS138Ly6R4nEhEREa+o\nvEWB9hlJrOh+HkUuFtDZNxERkYZM5S1KnHPMIJ4PDAMgccWnsHqmp3lERETEGypvUaJbZgrzO/6e\nkrIls3Z+rLNvIiIiDZHKWxQ569jDeTkwBIBGS96F9fM8TiQiIiK1TeUtivRpncaMNucScAZA4cd3\nepxIREREapvKW5QZd9ww3goeAkDcwtdg8xKPE4mIiEhtUnmLMgPbp/NZi98B4CNI0Yf/63EiERER\nqU0qb1Ho1GOH82ZgEADx81+CNT96nEhERERqi8pbFBrSJYO3mv2R4rInT4vevkFrnoqIiDQQKm9R\nyMw4b8TRu1ddiF/1Ffz8ocepREREpDaovEWpge3TmdPpQra6RgAUvn0DBAMepxIREZGapvIWxS45\neRAPB04FIGHLQpj1nMeJREREpKapvEWxTs2SKej/R9a4dACKPrgNigs8TiUiIiI1SeUtyl0yvA8P\nuNMBiN+5nuDXD3qcSERERGpSWOXNzAaa2QNmlm1mO8xshZlNNbOulRzf2MweMbONZeM/MbP+4WRp\n6JqlxJM19FzmB9sCEPjiXti+0eNUIiIiUlPCPfP2Z2A08BFwOfAIMBSYaWa99zXQzHzAW8CZwAPA\ntUBz4FMz6xJmngbtD0M78+/Y0MS9saU7KP1osseJREREpKaEW97uAdo55y5zzj3mnJsMDAFigOv2\nM3YMMBjU6+cqAAAgAElEQVQY75yb5Jx7EBgGBIBJYeZp0BLjYhh83P/wWaAvAP4fn4SV33ucSkRE\nRGpCWOXNOTfdOVe8177FQDbQYz/DxwDrgZfLjd0ITAVONbP4cDI1dGMGtObxtAkUuVgMR/Frl0Gg\n1OtYIiIiEmERe2DBzAxoAWzaz6H9gJnOueBe+78DEoF93jdnZs3NrFf5DegUbu76Isbv46JRx/JQ\n6QgA4jbNw33zkMepREREJNIi+bTpWUAr4IX9HJcFrK1g/659LfczfgIwd6/ttcrHrL8O6diUtX0u\nZkkwC4DAx7dD3kqPU4mIiEgkRaS8mVl34EHga+DJ/RzeCCiqYH9huff35SGg917bqZUOW8/9+eS+\n/M3/RwBiAjspfesajxOJiIhIJFW7vJlZJqGnR/OBMc65/a3RtBOo6L62hHLv/ybn3AbnXHb5DVhS\n1dz1VdPkeI45cSwvBw4HIGbxO7DgLY9TiYiISKRUq7yZWRrwDtAYON45t6YSw9YSunS6t137KvMZ\nsg9jB7Th7cwJ5LtEAIrfuBqKtnucSkRERCIh7PJmZgnAG4QeMDjZOTevkkNnAf3L5nsrbxBQACwK\nN5OE+HzGtWOGcmfgTADidqwh+LHmfhMREakPwl1hwU/owYRDgbHOua9/47gsM+tuZrHldk8j9FTq\nqHLHZQBjgTeccxXdDydV1LVFCqmH/YEfgqF5j+3bhyHnK49TiYiISHWFe+btbmAEoUum6WZ2dvmt\n3HF3APMJPYW6yzTgG+BxM7vZzCYAnwJ+4JYw80gFLju6G3c3uozCsrnfSl6+CIq2eR1LREREqiHc\n8nZg2Z+nAE9XsP2msgcaTiR05u4y4B+E5oY7yjm3MMw8UoFGcX4mjDmRO0tDC9fHbl1B8L0bPU4l\nIiIi1RHuCgvDnHP2W1u548aX7cvZa/wW59z5zrkM51xS2efNqObvIhU4vEsGgYEX8HWgJwC+mU/A\n4g+8DSUiIiJhi+QkvVJH/fnEnvwz+XK2udAUeqWvXAIFuR6nEhERkXCovDUAiXExXHP6cUwuPQeA\nmIL1BN+62uNUIiIiEg6VtwZiQLsmpB9+Hh8G+gHgy34J5r7scSoRERGpKpW3BuSK4V35T5M/keuS\nASh9/XLYstzjVCIiIlIVKm8NSHyMn5tOP5KbAmVrnxZvJTB1PJQWextMREREKk3lrYHp2TKVnkef\nzeOlxwHgXzsT98FNHqcSERGRylJ5a4AuPqITn7e/lFnBjkDZ6gvzXvc4lYiIiFSGylsD5PMZd51+\nMLfGX7178frAKxMgd6nHyURERGR/VN4aqKbJ8Vx/5glcW3oxAP6SbQReOBdKCj1OJiIiIvui8taA\nHdwhnQOHn8WjpScC4F//E+696z1OJSIiIvui8tbAXTi0I991uoyZwc4A2Iz/wA9PepxKREREfovK\nWwPn8xl3jhvAXxOuZaNLBSD41lWwfLrHyURERKQiKm9Ck6Q4bjn7WCYGrqLIxeALlhCYcpYm8BUR\nEamDVN4EgH5tmzB25BhuLD0PAH9hLoEpZ0DRdo+TiYiISHkqb7LbmAGtaTz49zxWegIA/g3ZuJcv\ngGDQ42QiIiKyi8qb7OG6E3owvePlfBboC4AtfAs+vd3jVCIiIrKLypvswe8z7jvzIO5O+zNLglmh\nnZ//A2Y+7W0wERERAVTepAKpCbH8c/yR/Ml3HVtcMgDujcth0XseJxMRERGVN6lQh4wkrj3rZC4o\nvYZCF4u5AMGpv4NVM7yOJiIi0qCpvMlvOrxLBuNGjmZiyWUEnOErLST4zFjY9LPX0URERBoslTfZ\np7EHtaHf8DN3TyHiK8wl+PRI2LbO42QiIiINk8qb7NeEYZ2IPfg87isdBYAvfwXBZ8bAzjyPk4mI\niDQ8Km+yX2bGLaf0YmG3S3iu9EgAfOvn4J4dC0XbPE4nIiLSsKi8SaX4fca9p/fj9VZX825gIAC2\n6jvcs+OguMDjdCIiIg2HyptUWkKsn/87dxAPpP+FjwL9ALAV03HPnwklhR6nExERaRhU3qRK0hJj\neeKPh/OPtOv5ItAbAFv6CW7q76C02ON0IiIi9Z/Km1RZRnI8T14wlMkpN/FtsDsAtvg93LTfQ6DE\n43QiIiL1W9jlzcySzWySmb1rZrlm5sxsfCXHji87vqItM9xMUntapCbw+AVHcGPizcwMdgbAFrwJ\nL46H0iJvw4mIiNRj1TnzlgHcDPQAZof5GTcD5+y1af6JKNGycSP+e8GRXBt/C7ODHUM7F7xZdg/c\nTm/DiYiI1FPVKW9rgSznXDvgmjA/4x3n3DN7bbrzPYq0SU/kkQuO4vK4W5kR7AqA/fxh2TQi2z1O\nJyIiUv+EXd6cc0XOuWpPs29mKWbmr+7niHc6NkvmvxcezbUJtzA90BMAy/kC9/QoKMz3OJ2IiEj9\n4vUDC58AW4ECM3vdzLrsb4CZNTezXuU3oFONJ5V96tgsmScvOoqbkm/m08ABANiqb3FPjoCCXI/T\niYiI1B9elbcC4AngEmAkcCdwNDDdzNrsZ+wEYO5e22s1llQqrU16Is9cNIw7Um/6ZSLftbNw/zkW\n8lZ4nE5ERKR+8KS8OeemOud+75x7yjn3qnPuJuA4oClww36GPwT03ms7tUYDS6VlpTXimYuG8q8m\n1/NK4DAAbPNi3GPDYd1cj9OJiIhEP68vm+7mnPsS+BY4Zj/HbXDOZZffgCW1ElIqpVlKPM9eeDiP\nN7uOx0pPAMC2ryP43+Nh2RcepxMREYludaa8lVkJpHsdQqqvSVIcz104mE/b/4nJJWcB4CveFnqI\nIftVj9OJiIhEr7pW3joCG70OIZGRHB/Df8cPZGOfP3J58QRKnB8LFuNeHA9fPwjOeR1RREQk6tR4\neTOzLDPrbmax5fY1q+C4E4EBwLs1nUlqT1yMj3vHHUjzw87hvJJr2O4SMBy8dz28+SctpyUiIlJF\nMdUZbGYTgcZAy7Jdp5hZ67LX9zvn8oE7gHOBDkBO2XvTzexHYAaQD/QHziN02fT26mSSusfnM244\nqSePpiTwP++k8J+4u8i0LfDD45C7FMY9CY2aeB1TREQkKlSrvAFXA+3K/TyqbAN4hlAxq8gLwEnA\nsUAiodUaHgUmOefWVzOT1FF/HNqR5qkjGTutCQ/6/kFf3zJY9hnusWOwM6dCU03XJyIisj/Vumzq\nnGvvnLPf2HLKjhlf/ueyfTc65/o55xo75+Kcc+2ccxNU3Oq/Uw9sxX1/PJGLYyfzduBgAGzzz7hH\nj4Jln3ucTkREpO6raw8sSAMwoF0TXph4FP9qcgP3l54GgBXm4Z46TQ8yiIiI7IfKm3iidZNEpl1y\nOD92nsgVxRMocrGYC4QeZHj5j1Bc4HVEERGROknlTTyTHB/Do787iKaDz2F08S2schmhN+a8iPvP\ncNiS42k+ERGRukjlTTzl9xk3ndyTP4wbyZjA7UwP9ATA1s/FPTIMfv7Q24AiIiJ1jMqb1Akj+7Xm\nsYuP57rEv/6ypNbOLbhnxsBHt0Gg1OOEIiIidYPKm9QZvVul8eplR/BJ+yu4rPgSClx8aELfL+6C\np0bA1rVeRxQREfGcypvUKelJcTz5+4PJOvwcRhTfxsJg2ZzPy7/CPXyYLqOKiEiDp/ImdU6M38df\nTuzBtWefyjm+O3i+dBgAVrAZnhkNH9wCpcXehhQREfGIypvUWcf2yuSly47h+axruaJ4AjtcfOiN\nr+6D/wyHjYu8DSgiIuIBlTep09qkJzL1wkNpdtg5jCiezLxg2Wpsa2fh/m8ofP+YJvUVEZEGReVN\n6ry4GB83nNSTv5xzKuf67+Dh0lMIOsNKd8JbV8Fz42D7Bq9jioiI1AqVN4kax/RswZt/OpqvOlzK\nWSXXs8alh95Y/D7uoUMg+xVvA4qIiNQClTeJKi1SE3jy9wcz/MSxjAjcyeuBQ4GyhxleHA9Tz4Ud\nm7wNKSIiUoNU3iTq+HzGeYd34NmJx/NQ0+u5tHgiuS459Oa8V3EPDoLsV70NKSIiUkNU3iRqdctM\n4bWJh9FyyNkcX/wP3g0MBMAKNsGL54bOxG3f6G1IERGRCFN5k6gWH+PnLyf04OGLT+AfaTdwWfFE\ntuw6C5f9Cu6Bg+DHZ/REqoiI1Bsqb1Iv9G/bhLcuH0rW4WdzXPGdv5yFK8yD1y6Bp06FzUs8Tiki\nIlJ9Km9SbyTE+vnLiT3490UncmfjG7mw+E+sd41Dby77DPfvwfDFPRAo8TaoiIhINai8Sb0zoF0T\n3r5sCN2GncEJpXfxbOnRAFhpIXw0CR4+HJZ94XFKERGR8Ki8Sb2UEOvnymO7MeXS45nW8irGFt3M\nz8GWoTc3LoAnT4aX/gjb1nsbVEREpIpU3qRe65aZwrSLBnPyKaMZwz/4e8npFOxaI3XOVNwDA+Cb\nhyFQ6m1QERGRSlJ5k3rP7zPOHdyed686hhW9LuSYonLTihRtg3f/HLqUuuQTj5OKiIjsn8qbNBiZ\naQk8eGZ/7vzDSdzZ+EbGF19LTrBF6M2N8+Hp02DKGXoqVURE6jSVN2lwDu+SwTuXD2Hg8P9hhAtd\nSt3uEkJvLnw7tELD+zdBYb63QUVERCqg8iYNUnyMn0uO7Mw7Vx3Lqt4XcWTR3bxYOhQAC5bA9H/h\n/nlg6H640mKP04qIiPxC5U0atFaNG3H/Gf146KITearFnzmlaDIzgl0BsJ25ofvhHjwYsl/RKg0i\nIlInqLyJAAPbp/PaJYdxzujTuCj2di4uvpxlu+6H27IstE7qY0dDzpee5hQREQm7vJlZsplNMrN3\nzSzXzJyZja/C+MZm9oiZbTSzHWb2iZn1DzePSHX5fMa4g9rw6bVH0nnYWYxwd3NzyblsdimhA1b/\nAE+cBE+PhDU/ehtWREQarOqcecsAbgZ6ALOrMtDMfMBbwJnAA8C1QHPgUzPrUo1MItWWHB/DVcd2\n48NrhlPc/3yOLL6XB0pPZaeLCx2w5GN4ZBi8cA5sXORpVhERaXiqU97WAlnOuXbANVUcOwYYDIx3\nzk1yzj0IDAMCwKRqZBKJmBapCfxtdF+mXXE8MztfytCie3mqdDglzh86YP7ruIcGwSsXQ+5Sb8OK\niEiDEXZ5c84VOefWhTl8DLAeeLnc520EpgKnmll8uLlEIq1rixT+O34gD154Iq+3upKjiu/ipcDh\nBJ1hLgizn8PdfxC8egnkLvM6roiI1HNePbDQD5jpnAvutf87IBHoWvuRRPbt4A7pvHjRofz13JN5\nLOM6jiv+O+/sWqnBBWDWM7gHDoLXJsKWHG/DiohIvRXj0fdmAZ9XsH9t2Z8tgTkVDTSz5kCzvXZ3\nilw0kd9mZhzZvTlHdG3GW3M68Y8PunH/5mwuj3mZ4/wzsGAp/Pg0btZzWN//gSFXQoZu4xQRkcjx\nqrw1Aooq2F9Y7v3fMgG4JeKJRKrA5zNOOaAlJ/TO5I2fOvO3j3rxr83ZXBHzEsP9P4TOxM1+Djd7\nCtbrNBhyNWT29jq2iIjUA16Vt51ARfe1JZR7/7c8BLy4175OwGsRyCVSJTF+HyP7teaUvi15bVZn\nJn/ck/ty53FJzKsc7/sen7nQBL/Zr0DX4+GwK6DdoV7HFhGRKOZVeVtL6NLp3nbtW/NbA51zG4AN\n5feZWeSSiYQhxu9j9IDWnHpgS16d1YW7Pu3FPZsWMiHmdU71fYXfHCx6N7S1GQSHXQ5dTwCf5skW\nEZGq8aq8zQKGmJlvr4cWBgEFgCbPkqgU4/cxZkBrRvZrxbtzu3H/xz24b/3PXOR/g9H+z4m3Ulj5\nLTx/JmR0hcGXQp9xEJuw/w8XERGhFp42NbMsM+tuZrHldk8DWgCjyh2XAYwF3nDOVXQ/nEjU8PuM\nk/pm8c7lQ7jldycxNetqDi/6Fw+WjmCrSwwdtGkRvH4p3NcbPv0bbN/obWgREYkK5qqx2LaZTQQa\nE3o69GJC87btWjfofudcvpk9AZwLdHDO5ZSN8wNfAr2BfwCbCD2I0BYY6JxbWMUcvYC5c+fOpVev\nXmH/PiI1xTnHd8ty+b/Pl/LtguWc7v+Y82PeIctyfznGH4/1HQeHXgLNe3iYVkREalJ2dja9e/cG\n6O2cy67q+OpeNr0aaFfu51H8cjbtGSC/okHOuYCZnUiouF1G6OnS7wmtuFCl4iYSDcyMQR2bMqhj\nUxau684jn3fhyFnHcyzfcH7M2/T1LcMCRfDj06Gtw1A4+ELodgL4/F7HFxGROqRaZ97qCp15k2i0\nNn8nT05fznPf5tCtKJvzY95muO+H0BOqu6S1gYF/gP7nQmK6d2FFRCRiqnvmTeVNxGM7ikp5aeYq\n/vvlMoK5y/id/33G+T8j1Qp2H+NiErBeo0JFrtUA0BPWIiJRS+UNlTepH4JBx0cLNvDk9Bxm/ryK\nkf4vOdf/Hl19q/c8MLNvqMT1GQtxSd6EFRGRsKm8ofIm9c/PG7bx5PTlvDRzJQeUzuEc/wcc65tB\njP0ys46LT8X6jIUB4yGrr3dhRUSkSlTeUHmT+mtrYQkv/bCKp79ZzvaNqzjd/wlnxHy8x1OqALTs\nFypxvUdDfIonWUVEpHJU3lB5k/rPOcfXSzfz7Dcr+DB7NUfwA2f4P+YI3097PODgYpOw3iPhwLOh\n7SG6N05EpA7yeqoQEakFZsbgThkM7pTBhq09eeH77tz4/VDIW8G4mM8Y5/+ULMvFSnbAj8+EtvRO\n0O8sOOAMSG3p9a8gIiIRojNvIlEqEHR8+fMmpny7gk/mr+EwZnG6/xOO9M0i1gK7j3PmwzoeGSpx\n3U+CuEQPU4uIiM68iTRQfp9xRNdmHNG1GRu29eKlH3pyx4wh/GXTGk7zf8k4/2d0863CXBCWfARL\nPsLFJWM9T4UDTod2h4OvxlfIExGRCNOZN5F6xDnHjOVbeHHGSt76aQ2dShYzxv85p/i/polt3/Pg\n1FahBxz6jIXMPro/TkSkluiBBVTeRCqyo6iUt+esZdoPq5i5bANH+n5kpP9LjvbNJK7cZVUAmnWH\nPmOg9xhI7+BNYBGRBkLlDZU3kf1ZtaWA12at4aWZq8jduI6T/N8ywj+dQb4Fvz641QDoNQp6jYS0\nVrUfVkSknlN5Q+VNpLKcc/y0Kp9XflzNmz+tIX77ak7xf82p/un08K349YC2h4aKXI9TIDWr9gOL\niNRDKm+ovImEozQQ5Kslm3lt1mrem7uOViU5nOz/mlN8X9PBt36PYx2GtT0Eep4aKnJprT1KLSIS\n/VTeUHkTqa6dxQE+WrCeN2ev5eOF6+kaWMIp/q852f8NrWzzrwe0OihU4nqcAk071X5gEZEopvKG\nyptIJG0rLOHD+et5Y/Zavli0nl5uCSf4v+VE33e08W389YBmPcqK3MmQ2VdPrYqI7IfKGypvIjUl\nr6CYD+dv4J05a/li8Ua6BJdykv9bjvN9Tyff2l8PSGsD3U4Ibe0Oh5i42g8tIlLHqbyh8iZSG7YW\nlvDJgg289dNaPl+8gdalKznON4Pj/d/Rx5fz6wHxqdD5mFCR63wMJKbXemYRkbpI5Q2VN5HaVlBc\nyueLNvFe9jo+mr+elMK1DPf/wHDfDwzyzSfGgnsc78yHtRkEXY+DrseH5pXT5VURaaBU3lB5E/FS\nSSDIN0s388G89Xwwbz078jcxzDeb4f4fOMI3m1Tb+etBaW2gy3DoPBw6DIX45NoPLiLiEZU3VN5E\n6grnHNlrtvLBvPV8OH89C9fkMtC3kKN8P3KU78cK75Nz/jis3eDQpdVOR0PzHjorJyL1msobKm8i\nddWavJ18snADH8/fwJc/b6JlYDVH+X7kCN9sBvnmE2+lvx6U0hI6HQWdjoSOR0JS09oPLiJSg1Te\nUHkTiQY7iwN8vXQTH83fwKcLN7IlbwuH+rIZ5pvNMN/sCqchcRiW1TdU4joOC634EJtQ69lFRCJJ\n5Q2VN5Fo45xjycbtfLpwI58u3Mh3yzbTKriGob6fGOr7iUN980i0ol8PjEmANoOg4xHQYRhkHQD+\nmFrPLyJSHSpvqLyJRLuC4lK+WbqZzxdt4vNFG1m1KY8BvkUc7pvDYb659LVl+KyCf1fFp0L7w6H9\nEOgwBJr3Ap+v9n8BEZEqqG550/+yiojnEuNiOKp7C47q3gKAlbkFfLG4P1/9fAyPLtmEK9jCob55\nDPHN4VBfNh1960IDi7bCwrdDG0CjJtDusNATrO0PD63+oDInIvWMzryJSJ0WCDrmrdnKlz9v4suf\nNzIjZwtNSzdwqG8eg/1zOcyXTaZtqXhwoybQdjC0GwztD4MWfXSZVUQ8p8umqLyJNCSFJQF+XJHH\n9CWbmL5kM7NXbqG1W8uhvnkc4pvPob55NLe8igfHpUCbgaFC1/YQaDUA4hJr9xcQkQbPs/JmZvHA\nX4FzgCbAT8CNzrkP9jPuVuCWCt4qcs6F9RiZyptIw7W9qJQZObl8szSXb5ZuZs7qPNq71RzsW8DB\nvgUM8s2npeVWONb5YrCsA0NFrs3B0OYQSGlRy7+BiDQ0Xt7z9gQwBrgPWAyMB942syOdc19WYvzF\nwPZyPweqkUVEGqjk+BiGdWvOsG7NAdhWWMKMnC18uyyXp5dt5ppVeWS6DRzim89BtpCBvoW7Jwu2\nYCmsnhHavi77wMbtQmWu9cDQ1qK3LrWKSJ0S1r+RzOxg4HTgGufcXWX7ngLmAncCgyvxMdOcc5vC\n+X4Rkd+SkhDLkd2bc2T3UJnbWRzgxxVb+GbZYbyek8ukFXkkFuZykG8RB/lCZa6X5RBrZf//mLc8\ntP30Qujn2ERo2T90ubX1wNCl1pRMj347EZHwz7yNIXSm7JFdO5xzhWb2H+B2M2vjnFu5n88wM0sF\ntrn6cOOdiNRJjeL8DO6cweDOGUBoLdbsNVuZkXMI3+fk8nDOFrbv2M4BtoQBvsUM8C1kgG8xTazs\nwkBJASz/MrTtktoaWvWH1geFylzWARCf4sFvJyINUbjlrR+wyDm3da/935X9eSCwv/K2FEgGdpjZ\nq8BVzrn1YeYREamUWL+PA9s05sA2jTl/SEeccyzfXMAPy7fww4ot3LV8CwvXb6U96+hvi+nvW0w/\n3890sxX4d801t3VVaJv/OlC2EkSzbqEzdK36h/5s0UurQYhIjQi3vGUBv15h+pd9LfcxdgvwAKE7\nTIqAIcAlwMFmdlAFhXAPZtYcaLbX7k6VCS0isjczo31GEu0zkhg9oDUAWwtLmL0yj5nL83h/xRb+\nvmILpYXb6etbygG2hAN9P3OgbwlZZQ9CGA42Lghts58LfbAvBpr3gJb9IOtAaHlgaBJhFToRqaZw\ny1sjQsVrb4Xl3q+Qc+6fe+16ycy+A54FJgB/2893T6Dip1VFRCIiNSGWIV2aMaRL6P8Tg0HH0k07\nmL0yj9mr8vj3yjzmr91KemAzB/iW0Ne3lL62lL6+pTS2HaEPCZbCujmhjaeA/2/vzoNkO8s6jn+f\n7pnp2e/sd1+Su+RecqESAomJlIoQlzJVYIkIoiwCQShKQMAlCBSiUiAUCBEVDSIiQoJoQBZLkRBZ\nDEEIyc2+3W32tfe9X/94T8/07dszd6YzW9/5fareOqff856Zt/upnvPMe857DjgLY0PHYMcz/KnW\nnc/wEyJauzfonYpII6o3eUsDkRr1rRXbl80591kz+xDwfC6cvH0cuK2q7iBw+0p+p4jIcoVCxqGh\nTg4Ndc6PzmXyRR4cjXHv2Sg/PjvHv52N8vhknH2M83R7kuOhJ3mGPcHx0Em6LQWAuSKMn/ClPEIH\n0HuJT+R2PN0ndtuPQ/cuMNuItysim1y9ydsosLtG/c5gOVLHzzwD9F2okXNuApiorDP9gRORddba\nHObKfb1cua93vi6eyXNiOMaJ4Sj3DUf5/HCUk1Nx9ts4x+0kx0Mnudye5Hjo5MKECIDZJ315oOJ/\n0LZen8RtPw47jsPQ0/xp2OZFT2yIyBZRb/J2D/BcM+uuukbtmorty2Y++zoA/KjO/oiIbLiu1mau\nPdjPtQf75+timTwnhqPcPxzj/pEoXxiJ8cRknJ1uistDJ3la6BSX2ymOhU6xxyrunpSehZP/40vA\nWQjru9RPhhi63CdzQ8f8yJ3uRSeyZdT7bf8C8DbgRqB8n7cI8CrgrvJtQsxsH9DunHuovKOZDTrn\nJqt+3uvxkxC+Xmd/REQ2pe7WZq47OMB1Bwfm61K5Ag+OxnlgNMYDIzG+ORLlobE4bYUox0KnOWan\nOWanOBo6zREbJmJ5AMyVYPoxXypH6cIRGDwCg8dg6CgMBqX3AITC6/yORWSt1ZW8OefuMrPbgPcF\nsz8fA16BHz17dUXTTwM/DVSe1zxlZp8H7sNPcHgO/oa/9wB/U09/REQaSXtLE1ft7+Wq/QunXAvF\nEk9MJXlwNMaDo3G+Mhbjg6MxpmIpLrFRjtoZjoZOc5md4Vjo9LmjdMVsxeSICuEIDBwJErujwfpl\n0HcQmlrW6d2KyGp7KuPsLwfey7nPNr3BOXfnBfb7J/wTGH4FP8HhFP6pDH/qnEs9hf6IiDSspnCI\nI9u7OLK9ixdcsVA/k8zx0GiMh8biPDwW5z/H4zwyFiecj3PYhjkcOstldpbDdpbLQmfYbnMLOxez\nMH6fLxWchf3p14EjMHA4WB6BgUP+WjsR2dTqfjD9ZqIH04vIVlIqOc7MpnhkPMEj4/GgJHh8MkFr\nIcYhG+FwaJgjQVJ3MDTCbpte3g9vH4D+Q0E5uLDsvQRa2tf2jYlsERv5YHoREdkAoZCxv7+D/f0d\nXP+07fP1xZLjzEyKRyd8UndiIsG/TsR5fCJJKJ/goI1wyIY5HBrmoI1w0EbYZxMLz3UFSE35cuZ/\nz//F3buh71KfzPUd9Ot9l/pr65TYiawbJW8iIheJcGjhaRGVSV2p5BiJpnl0IsHjEwkem0jwzckk\nj08miCZT7LMJDtkwl9gYl9gol4ZGucRGGbSqB97Ehn2pmAE7r2sX9F3iR+j6DgTL4HVbr+5ZJ7KK\nlEdorhsAABGpSURBVLyJiFzkQiFjT287e3rbee5lQ+dsm03meGIqweMTSZ6YSvLNqQSfnEpycjpF\nayHOARvjgI35xC40Or++zaouUY6P+HLqO+d3ININvfv9CF3vAejZH5R90LMXWjrW7L2LXIyUvImI\nbGG9HS1c1dHHVfvPvUd6seQYmUtzcjrJySCZ+/JUkienk5yZSdFe9IndfhvngI1zIDTGPptgv40z\naNFzf0k2Vns2bFnHIGzbu5DM9ewPXu+FbXugddsavXuRxqTkTUREzhMOGXv72tnb1z7/jNeycmJ3\najrFyekkp6aTfG06xZmZFKdnUpBNzidye2yCfRVlj00SscK5vyw56cvID2t3JrLNJ3HzZbdP7rp3\n+9fduyDcvEafhMjmo+RNRERWpDKxe87hgXO2OeeYSuQ4PZPi9EySMzNp7ptJ8ZWZFGdnUozFUgy4\nKHuDRG5vUHbbJLttit02RUvlBAqAbBQmojBRe1Kew7DOIZ/Ede8Olrv8dXjdO31d105NqpCLhpI3\nERFZNWbGYFeEwa7IOTchLssWiozOZTg7m+bsbIqzs2m+N5tieC7N2dk0E7EU/S7KnopkbpdNs6ti\nvfp6O8NBYtyXkSWesti6bSGh6wpKeb1zB3Rth87tGsWTTU/Jm4iIrJtIU3h+Rmwt+WKJsahP7kbm\nfHkwmuEbc2mG59KMzqUhm2CnTQdJ3TQ7bYYdzLDTptlhM+ywGbotff4Pz0R9mXxw0f45DGvvh64d\n0Dnkk7rOIZ/UlZO7jiFf17pNs2hlQyh5ExGRTaM5HJo/JVuLc45YpsBoNM3oXIaRaJqxaIa7oxnG\nYxlGoxnGoxlKmTg7bIbtNstOZuaTuu02O18GmSNs596o3nAL97obX7qvLhzxp2s7BnxC1zEInYN+\n2TEY1Afr7f0a0ZNVo+RNREQahpmxra2ZbW3NHN3RvWi7RLbAWJDQjUUzjMUyPBrL8J1YlrFYholY\nhul4ip7SHEM2x5DNsj1YDjHLoEUZsjkGbY5BoufeyLjcl2IWomd8WQbXug3rGPRPsegY8AldeTlf\n+qCtz69HujSyJzUpeRMRkYtOZ6SJQ0OdHBrqXLRNqeSYSeWYiGWZiGfmlw/Fs3w7kWUyXi5pWrJR\nBiwWJHNz88ldv0UZJEq/xRiwKP3EaLJSzd9n5dO2048t6z24UDO092FtfUFS1+vL/HpF3XzpgeZ2\nJX0XOSVvIiKyJYVCxkBnhIHOCE9j8VE8gGS2wFSQ0E0lskwmckzGs5xKZJlO5JhJ5phKZpmOpyGz\nkMj1WbxiPUa/xegnTp/5170kFk/2SvmFiRgr4ELN0NaDtfb46/LaeqC8Pv9620KJVKy3dkNTZEW/\nT9afkjcREZEL6Ig00RFpYn//hZ8GkS+WmE3mmAqSuumkT/AmkzkeSuaYSWaZTeaZTmaJJrMU0nP0\nEKePOH0Wp9fi9JCgr3JpCXpI0GsJeoiffzuVClbKL9w7rw4uHMFFurG2bVik25++jXQFiV7XQmnp\n9E/PiHRBpDN4XbGtuU0jgGtEyZuIiMgqag6HGOpuZai7dVntSyVHLJNnNpVnJpljLpVjNpVnLpXj\nbCrHfak80XSeaCrPbCrHXDJHLh2jORejxxJssyQ9JPw6SbZZkm5SwTJJtyXPqV9spK/MilksNQmp\n+pK/MmdhXHMHLtKJtXRgkS6spcMndi0dQalcryrNHT4BbOnwp4Jb2v0y3LLlk0IlbyIiIhsoFDJ6\n2lvoaW/hkkVuoVJLvlgils4zl15I7mKZvK/LFDgV1MWzeeKZArG035ZPx7FslLZSOblLsY0kXZai\nizRdlqKb1PyywzJ0kaLT0nSRppM0oapZurWYK2K5GORiT+XjOY+zMKWmNlxTG7S0Y83tWEtbsGz3\nCV9zOzS1+mVzq69ragvWy9vaKpYRv70pslDf1rOq/V5NSt5EREQaUHM4RH9nhP7OlV+j5pwjWygF\nyV6BWCZPIlMgkS0Qz/hkbzxTIJ4pkMz6+vmSzlPMJiCXwLJx2pxP7DpJ00EmSPbSdFjwmgztlqGT\nDB2Wpp0s7WToML/stMyK+m6uSDifgHwCatzObzXkw+00v3N0bX74KlDyJiIissWYGa3NYVqbwwx1\n1f9zyklgMlsglSuSyBZI5QokssX5ulSuwHS2GNQXSOeK8/WpXJFUNk8pl8LlklguheWThAsp2oPk\nro0sbZajnWywnqWNHG1k5utbydFqOVrJ0UaWdssSIUcredrIXvBUcbV4MUxf/R/LmlPyJiIiInWp\nTAL7V/Hnlko+KUznfZKXyRdJ50p+vVAinSuSLRRJ54rM5Ytk8iUy+SKZQpFMLnhdKPq6fIl8LovL\npyGfxgoZyKcJFf16uJSlqeQTvwh5nwRGIrxzFd/PalPyJiIiIptKKGS0tYRpawnT19Gy5r+vWHLk\nCiWyhSLZQol8cWUjdetNyZuIiIhsaeGKZLERhDa6AyIiIiKyfEreRERERBqIkjcRERGRBqLkTURE\nRKSBKHkTERERaSBK3kREREQaiJI3ERERkQZSd/JmZhEze7+ZjZhZ2szuMrPrl7nvbjO71czmzCxm\nZreb2aX19kVERERkq3gqI2+fAn4X+CfgTUAR+KqZPWepncysE/gm8NPAnwHvBq4EvmVmq/l0DRER\nEZGLTl1PWDCzq4GXAG93zn0wqPs0cAL4AHDdEru/ATgMXO2cuzvY92vBvm8FbqqnTyIiIiJbQb0j\nby/Cj7R9olzhnMsAtwDXmtneC+x7dzlxC/Z9CPgG8OI6+yMiIiKyJdSbvF0JPOKci1XVfz9YXlFr\nJzMLAc8AflBj8/eBg2bWVWefRERERC569T6YficwWqO+XLdrkf36gMgy9n14sV9sZkPAYFX1UYDH\nHntssd1ERERENoWKfKWlnv3rTd7agGyN+kzF9sX2o859y96An+Rwnhe+8IUX2FVERERk09gL/Gil\nO9WbvKXxI2jVWiu2L7Yfde5b9nHgtqq6TuAIftJD7gL71+sgcDvwAuDxNfodUh/FZnNSXDYvxWZz\nUlw2r9WOTQs+cftWPTvXm7yNArtr1O8MliOL7DeDH3XbWWPbhfYFwDk3AUzU2HTXUvs9VWZWXn3c\nOXf/Wv4uWRnFZnNSXDYvxWZzUlw2rzWKzYpH3MrqnbBwD3DEzLqr6q+p2H4e51wJuA94Vo3N1wBP\nOOfidfZJRERE5KJXb/L2BSAM3FiuMLMI8CrgLufcmaBun5kdrbHvs83sWRX7Xgb8LOefDhURERGR\nCnWdNnXO3WVmtwHvC2Z/Pga8AjgAvLqi6afxT1KwirqPA68FvmJmHwTy+Cc1jAMfqqc/IiIiIltF\nvde8AbwceC/wm0AvcC9wg3PuzqV2cs7FzexngA8Df4Qf/bsDeItzbvIp9GetTQLvCZayuSg2m5Pi\nsnkpNpuT4rJ5barYmHNuo/sgIiIiIsv0VB5MLyIiIiLrTMmbiIiISANR8iYiIiLSQJS8iYiIiDQQ\nJW8iIiIiDUTJm4iIiEgDUfJ2AWYWMbP3m9mImaXN7C4zu36j+7WVmNmzzexmM7vfzJJmdtrMbjWz\nIzXaHjOzr5tZwsxmzOwfzWxwI/q9FZnZO8zMmdmJGtuuM7Nvm1nKzMbM7KNm1rkR/dwKzOyZZval\n4HuQMrMTZvY7VW0Uk3VmZofN7HNmdjb43B8ys3eZWXtVO8VmjZhZp5m9JzhWzAR/s165SNtlHVPM\nLGRmv2dmT5pZxszuNbOXrtV7eCo36d0qPgW8CPgI8CjwSuCrZvZc59y3N7BfW8nvAz+Jf3zavcAO\n4I3AD83sJ5xzJwDMbA9wJxAFbgI6gbcBTzezq51zuY3o/FYRfP43Acka264AvgE8iH+iyh58bA4D\nv7iO3dwSzOzngC/jH3z9XiABHMR/7uU2isk6M7O9wPfxf6NuBmaAa/E3f70KeEHQTrFZWwPAu4DT\nwI+Bn6nVaIXHlD8F/gD4W+BufCw/a2bOOfe5VX8HzjmVRQpwNeCAt1XUteIfB/bdje7fVinAdUBL\nVd1hIAN8pqLu40AK2FdR9/wghjdu9Pu42AvwOfwB5w7gRNW2rwIjQHdF3WuC2PzcRvf9YipANzAG\nfBEILdFOMVn/2NwUfL6XV9X/Q1Dfq9isSxwiwI5g/VnB5/rKGu2WdUwBdgM54OaKOsMnfmeA8Gq/\nB502XdqLgCLwiXKFcy4D3AJcG/wXJWvMOfddVzVq5px7FLgfOFZR/SvAvzvnTle0+y/gEeDF69HX\nrcrMfgr/fXlzjW3dwPX4RDtWsenT+BEhxWZ1/TqwHXiHc65kZh1mds7fesVkw3QHy/Gq+lGgBOQU\nm7XnnMs658aW0XS5x5QXAM34ZK/czgF/hR81vXY1+l1JydvSrgQeqfoCgR/2BrhinfsjATMz/AFq\nKni9GxgCflCj+ffxsZQ1YGZh4GPA3znn7qvR5On4SzTOiU2QkN+DYrPang/EgN1m9jD+gB8zs78y\ns9agjWKyMe4IlreY2RVmttfMfg14PfBR51wSxWZTWOEx5Ur85SIP1mgHaxAzJW9L24n/j6hauW7X\nOvZFzvUy/FD154PXO4PlYvHqM7PIenRsC/ptYD/wzkW2Xyg2+h6trsP4g//twH/gRw8+iY/T3wdt\nFJMN4Jz7Ov57cj3+esTT+MsNPuace0vQTLHZHFZyTNkJjAejbdXtYA1ipgkLS2sDsjXqMxXbZZ2Z\n2VHgL4Hv4a8VgYVYXChetbZLncysH/hj4L3OuclFml0oNvoera5OoB34a+dceXbpF82sBXidmb0L\nxWQjncRfC/UvwDTwS8BNZjbmnLsZxWazWMkxZd1zBSVvS0vjL2ys1lqxXdaRme0AvoKf/fMi51wx\n2FSOheK1vv4EP2PuY0u0uVBsFJfVVf48/7mq/rPA6/DX36SCOsVkHZnZS/DXUB9xzp0Nqr8YXJP4\nfjP7Z/R92SxWckxZ91xBp02XNsrC0Gmlct3IOvZlyzOzbcDXgB7gF5xzlZ9/eXh6sXjNOOc06raK\nzOwwcCPwUWCXmR0wswP4P1jNwes+LhwbfY9WV/nzrL4ofiJY9qKYbJQ3AD+qSNzKvoQfLb0SxWaz\nWMkxZRTYEVyLXd0O1iBmSt6Wdg9wJJj9U+maiu2yDoILrb8MHAFucM49ULndOTcMTOKnfVe7GsVq\nLezG/w35KPBkRbkGH6cn8fdSOgEUqIpNcBrvChSb1fZ/wXJ3VX35uptJFJONsh0I16hvDpZNKDab\nwgqPKffgk+9jVe3WLFdQ8ra0L+C/aDeWK4ILFF8F3OWcO7NRHdtKgtmMn8ef7vlV59z3Fmn6L8AN\nlbdwMbPn4ROJ29a8o1vPCeCXa5T78Rdi/zJwi3MuCvwX8Btm1lWx/2/ir89SbFbXrcHy1VX1r8En\nBXcoJhvmEeDKGk+HeSn+ViH3KjabynKPKbcDefzIarmd4ScJDQPfXe2O2fmTI6SSmd2KPwh9GH9z\n3lfgs+7nOefu3Mi+bRVm9hHgTfiRt1urtzvnPhO024ufwTUH/AX+D93bgbPAs3XadH2Y2R3AgHPu\neEXdM/F/wB7AX/OzB3grcKdz7uc3op8XMzO7Bfgt/PflW/g7yP8q8D7n3E1BG8VknQX3Q/xv/ESF\nm4PlDfinJvydc+61QTvFZo2Z2Rvxl+Dswt+q5Yv44wf42b/RlRxTzOwDwbZP4J+w8EL8ZJSXOec+\nu+pvYKPvdLzZC/76nT/Hn9PO4O/b8vMb3a+tVPD3RnKLlaq2l+Nvj5AEZoHPANs3+j1spUKNJywE\n9c8BvoO/eHcCf/Dq2uj+XowFfxru3fiZjTn8o/3erJhsfMH/8//V4JiSAx7GP3mhSbFZ1zicXOK4\ncqCi3bKOKfgzmX8Y/Nws/szEy9aq/xp5ExEREWkguuZNREREpIEoeRMRERFpIEreRERERBqIkjcR\nERGRBqLkTURERKSBKHkTERERaSBK3kREREQaiJI3ERERkQai5E1ERESkgSh5ExEREWkgSt5ERERE\nGoiSNxEREZEGouRNREREpIEoeRMRERFpIP8P7Eczs8Y4rC8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11385eac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.201556\n",
      "Epoch 96, train loss: 0.201637\n",
      "Epoch 97, train loss: 0.199430\n",
      "Epoch 98, train loss: 0.197397\n",
      "Epoch 99, train loss: 0.195466\n"
     ]
    },
    {
     "data": {
      "image/png": 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Anzky88awpWDPEc4QERGR+ijS0aYPAecRvvKWYWYHTMrrnHu15OUDwBVAGyCv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\nwZuIiIjIFvL/AasxVkdCEwOUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e7d9a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.178427\n",
      "Epoch 96, train loss: 0.196650\n",
      "Epoch 97, train loss: 0.194443\n",
      "Epoch 98, train loss: 0.192334\n",
      "Epoch 99, train loss: 0.190332\n"
     ]
    },
    {
     "data": {
      "image/png": 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MVN6CVB7VFYCE6u0eJxEREQnoCCtIdASt/eeg8hakypjALgvaIktERNoDn89HTU2NCpzH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\n+X+tOhSgkaztZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8c2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.206326\n",
      "5-fold validation: Avg train loss: 0.193289, Avg test loss: 0.197213\n"
     ]
    }
   ],
   "source": [
    "train_loss, test_loss = k_fold_cross_valid(k, epochs, verbose_epoch, X_train,\n",
    "                                           y_train, learning_rate, weight_decay)\n",
    "print(\"%d-fold validation: Avg train loss: %f, Avg test loss: %f\" %\n",
    "      (k, train_loss, test_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fine-tuning, even though the training loss can be very low, but the validation loss for the $k$-fold cross validation can be very high. Thus, when the training loss is very low, we need to observe whether the validation loss is reduced at the same time and watch out for overfitting. We often rely on $k$-fold cross-validation to fine-tune parameters.\n",
    "\n",
    "## Make predictions and submit results on Kaggle\n",
    "\n",
    "Let us define the prediction function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,\n",
    "          weight_decay):\n",
    "    net = get_net()\n",
    "    train(net, X_train, y_train, None, None, epochs, verbose_epoch, \n",
    "          learning_rate, weight_decay)\n",
    "    preds = net(X_test).asnumpy()\n",
    "    test['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])\n",
    "    submission = pd.concat([test['Id'], test['SalePrice']], axis=1)\n",
    "    submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fine-tuning parameters, we can predict and submit results on Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96, train loss: 0.171151\n",
      "Epoch 97, train loss: 0.170580\n",
      "Epoch 98, train loss: 0.170049\n",
      "Epoch 99, train loss: 0.169546\n"
     ]
    },
    {
     "data": {
      "image/png": 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NLVV4ExERkcGj8NZPE4qyCZj3WuFNREREkk3hrZ8y0oKMG+WdcarwJiIiIsmm8DYAppTk\nAgpvIiIiknwKbwNgatx2Ic45n6sRERGR4UzhbQBMLs4GoCUcYV9ju8/ViIiIyHCm8DYAppTmHnit\nY7JEREQkmRTeBsDUuL3ettYovImIiEjyKLwNgPLCLNKD3j9KLVoQERGRZEoovJlZhZndZ2abzazF\nzKrN7Fkzu6iX9xea2a/NrMrMms3sKTNbkEgtQ0EwYEyMzXvTsKmIiIgkU6I9b5OAPOD3wBeB78Xa\n/2ZmnzrSjWYWAB4BrgZuAb4OjAaeNrMZCdbju4MH1Df5XImIiIgMZ2mJ3OScexR4NL7NzG4BXgW+\nAvz6CLdfAZwCXOmcWxq7915gI3ATXqhLOV3z3rbXthCJOoJdxy6IiIiIDKABm/PmnIsAO4DCo1x6\nBbAXuD/u3irgXuASM8sYqJoGU1fPW0fEsauu1edqREREZLjqV3gzsxwzKzGzaWb2ZeB9wD+Octt8\n4DXnXLRb+8tANjCzPzX5ZXLcitPNGjoVERGRJElo2DTOT4BPx15H8XrTrj/KPWXAsz20V8aey4HV\nh7vZzEYDpd2apx210iSL3y5kS3UzZx3jYzEiIiIybPU3vP0MWIoXuD4ABIH0o9yTBfR0DEFb3OdH\nch1wYx9qHBSleRnkpAdpDke0XYiIiIgkTb+GTZ1z651zTzrn/uCcuxDIBR4ysyPN1m8FeprXlhn3\n+ZHcCszp9rikb5UPPDNjSunBM05FREREkqG/PW/dLQV+hTdvbcNhrqnEGzrtrqtt95F+wDm3D9gX\n33bkrDh4JhfnsGbXfoU3ERERSZqBPmGha8iz4AjXvA4siO33Fm8x0IK3ZUhK6pr3tqu+lbaOiM/V\niIiIyHCU6AkLo3toCwEfxRv2fDPWVmZms2KfdVkKjAEuj7u3BLgSeMg519N8uJTQNWzqnLffm4iI\niMhAS3TY9Fdmlo+3anQXMBa4BpgFfNU517VXxg+Ba4EpwNZY21JgOXC7mc0GqvEWIQQZggsR+mJK\nSe6B11uqm5k5Js/HakRERGQ4SjS83QN8AvgsUAw04p2u8A3n3N+OdKNzLmJm5wM/Ar6AN9T6CrDE\nOXe4eXIpYUrxoduFiIiIiAy0RI/Huhu4uxfXLQGW9NBeB3wy9hg2CrJDFOWkU9scZosOqBcREZEk\nGOgFCyPewQPqFd5ERERk4Cm8DbCu8PbWvkaccz5XIyIiIsONwtsAqyjPB6CupYOdOqBeREREBpjC\n2wCbO77wwOs3djb4WImIiIgMRwpvA6yiPJ+0gHfiw6qd9T5XIyIiIsONwtsAywwFOWast7/bqh0K\nbyIiIjKwFN6SYN4Eb+h09a4GIlEtWhAREZGBo/CWBPPGe0e7toQjbKpqOsrVIiIiIr2n8JYEXT1v\nAK9r6FREREQGkMJbEkwvzSUrFATgDS1aEBERkQGk8JYEacEAx43zhk5X7dB2ISIiIjJwFN6SZG5s\n3tv6Pftp64j4XI2IiIgMFwpvSdI1760j4lhXud/nakRERGS4UHhLknk6aUFERESSQOEtSSYUZTEq\nOwTopAUREREZOApvSWJmB8451UkLIiIiMlAU3pKoa97b5upm9rd1+FyNiIiIDAcKb0nUddKCc7BG\n895ERERkACi8JdHcuEULqxTeREREZAAovCVRaV4G4wqzAM17ExERkYGh8JZk8yZ4Q6c6JktEREQG\ngsJbknUNne5uaGNfY5vP1YiIiEiqU3hLskM269U5pyIiItJPCm9Jdtz4Asy819qsV0RERPpL4S3J\ncjPSmF6aC2jFqYiIiPSfwtsgiD9pIRp1PlcjIiIiqUzhbRAsmOSFt4bWDtbvafS5GhEREUllCm+D\n4LTpJQdeP/92lY+ViIiISKpTeBsEk4pzmFDkbdb73FvVPlcjIiIiqUzhbZCcPqMUgJe31NLWEfG5\nGhEREUlVCYU3M1tkZreY2Vozazaz7WZ2r5nN7MW9S8zMHeYxNpF6UsHpsaHT9s4oK7bW+VyNiIiI\npKq0BO/7BnAqcB/wBjAWuB54zcxOcs6t6cV33ABs6dY2bDdCO2VaCQGDqIPn3qritBklR79JRERE\npJtEw9tPgaudc+GuBjO7B1gN/Cvw4V58x2POuRUJ/n7KKcgOMXd8Ia/vqOe5t6r5pt8FiYiISEpK\naNjUObcsPrjF2t4C1gLH9vZ7zCzPzIKJ1JCKTo/1tr1ZuZ+qxnafqxEREZFUNGALFszMgDFAb5dT\nPgXsB1rM7G9mNqOXvzPazCriH8C0xKoeXF2LFgCWbdKqUxEREem7gVxteg0wDrjnKNe1AHcAnwMu\nA/4TOBtYZmYTevE71wFruj0eTKzkwTV/YiE56V5Ho7YMERERkUQkOuftEGY2C/gl8CLw+yNd65y7\nF7g3rukBM3sCeBb4FvCZo/zcrXgLJeJNIwUCXCgY4ORpxTy5bh/PvVWFcw7rOrVeREREpBf63fMW\n297jEaABuMI51+dNzJxzzwMvAef04tp9zrm18Q9gU19/0y9dpy3s3d/O2/uafK5GREREUk2/wpuZ\nFQCPAYXAec653f34uh1AUX/qSQWnxc17e1ZDpyIiItJHCYc3M8sEHgJmAhc6597sZy1TgWF/8Oe0\n0hzKCzIBeP6tYf/HFRERkQGW6AkLQbyFCScDVzrnXjzMdWVmNsvMQnFtpT1cdz6wEHg8kXpSiZkd\n2KB3+eZa2jt1VJaIiIj0XqILFn4CXIzX81ZkZodsyuucuzP28ofAtcAUYGusbZmZrQRW4M2TWwB8\nHG/Y9AcJ1pNSTptRyr0rdtLaEeG1bfWcPK3Y75JEREQkRSQa3o6PPV8Ue3R3Zw9tXe4BLgDOBbKB\nSuA24Cbn3N4E60kpp00vwQxc7KgshTcRERHprURPWDjLOWeHe8RdtyTWtjWu7dvOufnOuULnXLpz\nbpJz7rqREtwAinLSqSjPB+D5t7VoQURERHpvIDfplT7oOm1h9a4GapvDR7laRERExKPw5pOzZnrh\nzTn4+9o9PlcjIiIiqULhzScnTC6iJDcDgEdWV/pcjYiIiKQKhTefBAPG+ceNBWDZphoNnYqIiEiv\nKLz56PzjygCIRB1PaOhUREREekHhzUeLJhdRmucNnT6qoVMRERHpBYU3HwUDxvlzNHQqIiIivafw\n5jMNnYqIiEhfKLz57ITJRYyODZ0+8oaGTkVEROTIFN58FgwY7zswdFpNTVO7zxWJiIjIUKbwNgRc\nMLccgKiDJ9aOmFPCREREJAEKb0PACZNGHRw6Xb3b52pERERkKFN4GwICATuwcOHFTTUaOhUREZHD\nUngbIi6Y64W3qIPHtepUREREDkPhbYhYOHEUY/K1Ya+IiIgcmcLbEBEIGO+bc3DotFpDpyIiItID\nhbch5MK4odMHVu7yuRoREREZihTehpCFk0YxtSQHgLte3o5zzueKREREZKhReBtCzIwPnTgBgE1V\nzbyytc7nikRERGSoUXgbYt6/YDyhoAFe75uIiIhIPIW3IaY4N4P3VnjHZT2yupL6lrDPFYmIiMhQ\novA2BF194kQAwp1R/qqFCyIiIhJH4W0IOmlqMZOKswEtXBAREZFDKbwNQYGA8aFFXu/bxr1NvLZd\nCxdERETEo/A2RF2xcDxpAW/hwp9f2uFzNSIiIjJUKLwNUaV5GZxbMQaAR1bvpqG1w+eKREREZChQ\neBvCrootXGjriPLg61q4ICIiIgpvQ9qp00qYUJQFwJ9f0sIFERERUXgb0uIXLqzf08jKHfU+VyQi\nIiJ+Syi8mdkiM7vFzNaaWbOZbTeze81sZi/vLzSzX5tZVez+p8xsQSK1DHdXxi1c+O1zW3yuRkRE\nRPyWaM/bN4D3A/8Avgj8GjgDeM3M5hzpRjMLAI8AVwO3AF8HRgNPm9mMBOsZtkbnZ3Lx8eUAPLam\nkq3VzT5XJCIiIn5KNLz9FJjknPuCc+43zrnvA6cDacC/HuXeK4BTgCXOuZucc78EzgIiwE0J1jOs\nffqMaQBEHdz23GafqxERERE/JRTenHPLnHPhbm1vAWuBY49y+xXAXuD+uHurgHuBS8wsI5GahrNj\nxubx7lmjAbjv1Z1UNbb7XJGIiIj4ZcAWLJiZAWOA6qNcOh94zTkX7db+MpANHHHenJmNNrOK+Acw\nLdG6U8Wnz5gKeOed/n7ZVn+LEREREd8M5GrTa4BxwD1Hua4MqOyhvaut/Cj3Xwes6fZ4sPdlpqYT\npxQxf2IhAH94cSvN7Z3+FiQiIiK+GJDwZmazgF8CLwK/P8rlWUBP435tcZ8fya3AnG6PS3pdbIoy\nswNz3/a3dXLXy9t9rkhERET80O/wZmZj8VaPNgBXOOciR7mlFehpXltm3OeH5Zzb55xbG/8ANvW1\n7lT0ntljmFqSA8Bvn99CR6T7yLOIiIgMd/0Kb2ZWADwGFALnOed29+K2Sryh0+662nrzHSNSMGB8\nKjb3rbKhjYdW6R+ViIjISJNweDOzTOAhvAUGFzrn3uzlra8DC2L7vcVbDLQAGxOtaSS4dP44SvO8\njstfPbNZR2aJiIiMMImesBDEW5hwMnClc+7Fw1xXZmazzCwU17wUb1Xq5XHXlQBXAg8557QPxhFk\nhoJ8/NQpAGzY28g/1+/zuSIREREZTIn2vP0EuBhvyLTIzD4c/4i77ofAOrxVqF2WAsuB283sBjO7\nDngaCAI3JljPiHL14onkZqQBcPOTG4lG1fsmIiIyUiQa3o6PPV8E/LGHx2HFFjScj9dz9wXgR3h7\nw73bObchwXpGlIKsEJ84zet9W7NrP4+t2eNzRSIiIjJYEj1h4SznnB3uEXfdkljb1m731znnPumc\nK3HO5cS+b0U//ywjyidPn8KobG80+if/u4FOrTwVEREZEQZyk14ZRHmZIT73rukAbK5q5v7Xdvlc\nkYiIiAwGhbcU9uGTJlFW4G2P97MnN9LWcbQt9kRERCTVKbylsMxQkC+cPQOA3Q1t/OklnbogIiIy\n3Cm8pbgrF45nSuzUhV8+9TZNOvNURERkWFN4S3FpwQBfec9MAGqbw/zu+S0+VyQiIiLJpPA2DFxw\nXBmzy/IBuO3ZzdQ1h32uSERERJJF4W0YCASMr513DACN7Z3c8tTbPlckIiIiyaLwNkycNbOUE6cU\nAfD7ZVt5a2+jzxWJiIhIMii8DRNmxo0XzSZg0Bl1fOehtTq0XkREZBhSeBtGKsoLuGbxJABeeLuG\nx3VsloiIyLCj8DbMfPXcmQeOzfrew2/SGtbGvSIiIsOJwtswU5idztfeOwvwNu699WktXhARERlO\nFN6GoQ8umsBx4woA+NWzm9lW0+xzRSIiIjJQFN6GoWDAuOmSCgDCnVG+9/CbPlckIiIiA0XhbZha\nMHEUVywcD8CT6/bx1Pp9PlckIiIiA0HhbRj7xnmzyMtIA+D/PbiGZp17KiIikvIU3oax0rwM/uW9\n3skLO+ta+c/H1/tckYiIiPSXwtsw95GTJnHi5NjJCy9u4+UttT5XJCIiIv2h8DbMBQLGf1wxl4w0\n76/660tXae83ERGRFKbwNgJMKcnha7Hh0601Lfzk7xt8rkhEREQSpfA2Qnzs1CnMn1gIwG9f2MKr\n2+p8rkhEREQSofA2QgQDxo+umEt6WgDnvOHTtg4Nn4qIiKQahbcRZProPL50zgwANlU187Mn3/K5\nIhEREekrhbcR5lOnT407OmsTL26q8bkiERER6QuFtxEmLRjgpx+YR2bIGz790j0rqW0O+12WiIiI\n9JLC2wg0Y0weN1zonX26d387X1/6Bs45n6sSERGR3lB4G6GuOnEC75szFoAn1+3lj8u3+VyRiIiI\n9IbC2whlZvz75XMpL8gE4PuPrGNd5X6fqxIREZGjUXgbwQqyQ/z8qvkEDMKdUT5/10qdviAiIjLE\nJRzezCzXzG4ys8fNrNbMnJkt6eW9S2LX9/QYm2hN0neLJhfxxbNnAvD2viZuemitzxWJiIjIkaT1\n494S4AZgO7AKOCuB77gB2NKtrb4fNUkCrn/3dF7YVM3LW2q5+5UdLJg4ig8smuB3WSIiItKD/oS3\nSqDMObfHzE4AXkngOx5zzq3oRw0yAIIB4+cfOp4Lf/E8Nc1hvv3AGo4Zm8e8CYV+lyYiIiLdJDxs\n6pxrd87tqZVPAAAgAElEQVTt6W8BZpZnZsH+fo/0T1lBFr+8ZgHBgBGORPnMna9S3dTud1kiIiLS\njd8LFp4C9gMtZvY3M5vhcz0j2klTi/nW+ccCUNnQxuf+9BodkajPVYmIiEg8v8JbC3AH8DngMuA/\ngbOBZWZ2xMlWZjbazCriH8C0ZBc8Unzs1MlcNn8cAC9tqeWHj673uSIRERGJ1585bwlzzt0L3BvX\n9ICZPQE8C3wL+MwRbr8OuDGJ5Y1oZsYPLjuODXsaebNyP797YQvHjc/nsvnj/S5NRERE8H/Y9ADn\n3PPAS8A5R7n0VmBOt8clya1uZMlKD/KrjyykMDsEwL/+ZTUrt9f5XJWIiIjAEApvMTuAoiNd4Jzb\n55xbG/8ANg1OeSPHhKJsbrnKW8DQ3hnlk79fwfaaFr/LEhERGfGGWnibClT5XYR4TptRwncv8Q6w\nr2kO87E7XqahpcPnqkREREa2pIc3Myszs1lmFoprK+3huvOBhcDjya5Jeu+axZP49BlTAdhU1cyn\n71xBe6eO0BIREfFLvxYsmNn1QCFQHmu6yMy6Zrb/l3OuAfghcC0wBdga+2yZma0EVgANwALg43jD\npj/oT00y8L5x3ix21LXw6Oo9LN9cyzf/spqffGAeZuZ3aSIiIiNOf1eb/gswKe795bEHwJ14wawn\n9wAXAOcC2XinNdwG3OSc29vPmmSABQLGTz9wPHsalvPa9nruX7mLCUXZfPk9M/0uTUREZMTp17Cp\nc26yc84O89gau2ZJ/PtY27edc/Odc4XOuXTn3CTn3HUKbkNXZijIbR89gYlF2QD8/B9vcefybT5X\nJSIiMvIMtQULMoQV52Zwx8cWHdhC5P89uIYHX9/lc1UiIiIji8Kb9MnU0lx+/7ETyUkP4hx89d5V\n/HO9OkxFREQGi8Kb9Nm8CYX85tpFpKcF6Iw6PnvnayzfXON3WSIiIiOCwpsk5ORpxdx69aGb+L6x\ns97vskRERIY9hTdJ2Dmzx/CTK+dhBk3tnVz7u5fZsKfR77JERESGNYU36ZdL54/juxd7pzDUtXRw\n1W3LWb9nv89ViYiIDF8Kb9JvHzl5Mt983ywAapvDXPXr5by5WwFOREQkGRTeZEB8+sxp/N/zvQBX\n19LBNb9Zztrdh9ujWURERBKl8CYD5lNnTOPbFxwLdAW4l1izSwFORERkICm8yYD65OlTueHC2QDU\nxwKcVqGKiIgMHIU3GXAfP20K37nIC3ANrR1c9evlLNtU7XNVIiIiw4PCmyTFklOn8P1L52AGzeEI\nS373Ck+s3eN3WSIiIilP4U2S5sMnTeLnH5pPWsAIR6J89s5XuXfFDr/LEhERSWkKb5JUF88r5zfX\nnkBmKEDUwdeXvsFvntvsd1kiIiIpS+FNku6sY0bzp08uJj8zDYDvP7KOHzy6jmjU+VyZiIhI6lF4\nk0GxcFIR93z6ZErzMgD49bOb+fxdK2nriPhcmYiISGpReJNBc2xZPvd/9hSmleYA8MjqSq75zUvU\nNod9rkxERCR1KLzJoJpQlM39nz2VxVOKAHh1Wx2X3/oCW6qbfa5MREQkNSi8yaAryA7xh0+cyKXH\nlwOwtaaFy299gVe21vpcmYiIyNCn8Ca+yEgLcvMHj+fz754OeMdpXX3bcu56ebvPlYmIiAxtCm/i\nGzPjq+cew39eMZdQ0OiIOL55/2pueHANHZGo3+WJiIgMSQpv4rsPnDCBu/7PSZTkpgPwhxe38eHf\nvERNU7vPlYmIiAw9Cm8yJJwwuYi/XX8ax40rAOClLbVcfMsLrN3d4HNlIiIiQ4vCmwwZ5YVZ3PeZ\nk7kktpBhV30rl9+6jHtf0ZFaIiIiXRTeZEjJDAX52QeP55vvm0XAoL0zytf/8gb/ct8qWsPa0FdE\nREThTYYcM+PTZ07jzk8upiTXO5Fh6as7uezWF9hc1eRzdSIiIv5SeJMh65RpJTz6hdMObOi7fk8j\nF9/yAg+/sdvnykRERPyj8CZD2uj8TP70ycV89qxpADS1d3L9n1fyjaVv0BLu9Lk6ERGRwafwJkNe\nWjDAN86bxW+vPYHC7BAA96zYwYW/eJ41u7QaVURERpaEw5uZ5ZrZTWb2uJnVmpkzsyV9uL/QzH5t\nZlVm1mxmT5nZgkTrkeHv7GPH8NgXT+ekqd4w6ubqZi679QVue3Yz0ajzuToREZHB0Z+etxLgBuBY\nYFVfbjSzAPAIcDVwC/B1YDTwtJnN6EdNMsyVFWTxp0+exNfeewzBgHcqw789uo5rb3+ZyoZWv8sT\nERFJuv6Et0qgzDk3CfhaH++9AjgFWOKcu8k590vgLCAC3NSPmmQECAaMz71rOks/czITi7IBeO6t\nat5787P8deVOnFMvnIiIDF8JhzfnXLtzbk+Ct18B7AXuj/u+KuBe4BIzy0i0Lhk55k8cxSNfOI0r\nFo4HYH9bJ1++ZxWfvfM1Ha0lIiLDll8LFuYDrznnup8+/jKQDcwc/JIkFeVlhvjxlfO47aMnHDgb\n9fG1ezj35md5fE2i/99CRERk6PIrvJXhDbt219VWfrgbzWy0mVXEP4BpyShSUsd7Zo/h718+k/OP\nGwtATXOYz9z5Ktf/+TWq1QsnIiLDiF/hLQvo6X9R2+I+P5zrgDXdHg8OaHWSkopy0vnl1Qv4+YeO\npyDL21Lk4TcqOeenz2gunIiIDBt+hbdWoKd5bZlxnx/OrcCcbo9LBrQ6SVlmxiXHj+N/v3wG51V4\nvXD1LR18+Z5VfOyOV9hVrxWpIiKS2vwKb5V4Q6fddbUd9vwj59w+59za+AewKRlFSuoanZ/J/3xk\nIf99zYID56M+vaGKc3/6DL95bjOdke7TLUVERFKDX+HtdWBBbL+3eIuBFmDj4Jckw9H7jivjya+c\ncWBFanM4wvcfWceF//U8r26r9bk6ERGRvkt6eDOzMjObZWahuOalwBjg8rjrSoArgYecc5phLgOm\nMDudH185jzs/sZgpJTmAd8j9+//7Rf71L29Q1xz2uUIREZHeS+vPzWZ2PVDIwdWhF5nZ+Njr/3LO\nNQA/BK4FpgBbY58tBZYDt5vZbKAabyFCELixPzWJHM5pM0p47Iun86tnNvPLp98m3Bnl7ld28MTa\nPXz13GO46sSJBAPmd5kiIiJHZP1ZgWdmW4FJh/l4inNuq5ndQSy8Oee2xt07CvgRcCne6tJXgH9x\nzq1IoI4KYM2aNWuoqKjo6+0yAm2raeaGB9fyzMaqA23HluVz40WzOWlqsY+ViYjIcLd27VrmzJkD\nMCc2d79P+hXehgqFN0mEc44n1u7hew+vO2QV6gXHlfHN82cxflS2j9WJiMhw1d/w5teCBRHfmRnn\nzSnjH189k6+8ZyaZIe9fh0dWV3L2T57hx09soKm90+cqRUREDqXwJiNeZijIF86ewT+/ehYXz/Om\nb7Z3Rrnlqbc560dP8+eXtmtrERERGTIU3kRiyguz+MVV81n6mZOZN74AgOqmdv7vX1dz/i+e46kN\n+3RKg4iI+E7hTaSbEyYX8dfrTuXnHzqecYXeSW0b9zbxsdtf4erbXuK17XU+VygiIiOZwptIDwIB\n75itf3z1TL5x3izyMrxddV7cXMPlty7jk79fwfo9+32uUkRERiKFN5EjyAwF+exZ03j6a2fx8VOn\nkB70/pV5ct1e3vfz5/ji3SvZUt3sc5UiIjKSKLyJ9EJxbgY3XDSbp752Fh9aNIFgwHAOHnx9N+f8\n9Bn+5b5VbK9p8btMEREZARTeRPpgXGEW//7+ufzvl8/gotjK1EjUsfTVnbzrJ0/zjaVvsKNWIU5E\nRJJH4U0kAVNLc/mvq+bz2BdP57yKsYAX4u5ZsYN3/fhp/vUvb7CtRsOpIiIy8BTeRPrh2LJ8/ucj\nC3nkC6fxntljAOiMOu5+xQtxX7p7JRv3NvpcpYiIDCcKbyIDoKK8gNs+egIPXX8a5xw7GoCogwde\n3825Nz/Lp/+4gjd21vtcpYiIDAdpfhcgMpwcN76A31y7iHWV+/nlU2/zyOpKnIMn1u7libV7OWVa\nMZ86YypnzizFzPwuV0REUpDCm0gSHFuWzy1XL+ArVU3899Ob+OvKXXRGHcs21bBsUw2zxubxqTOm\nctG8ckJBdYCLiEjv2XA47sfMKoA1a9asoaKiwu9yRN5hV30rv3t+C3e/vJ3mcORAe1lBJh89eTJX\nnTiBwux0HysUEZHBsnbtWubMmQMwxzm3tq/3K7yJDKKGlg7ufGkbt7+wleqm9gPtWaEg7184jiWn\nTGH66FwfKxQRkWRTeEPhTVJPW0eEB1bu4ncvbGHj3qZDPjvrmFKuPXkyZ84sJRDQvDgRkeGmv+FN\nc95EfJAZCvKhEyfywUUTeOHtGn77/Gae2lAFwNMbqnh6QxWTirP58OJJXHnCeA2piojIAep5Exki\nNlU1cccLW/nLaztpiZsXlxkKcMm8cXz4pEkcN77AxwpFRGQgaNgUhTcZXva3dXD/qzv5w/JtbK46\n9JSG48YVcM3iiVw0r5ycDHWci4ikIoU3FN5keHLO8cLbNfz+xa38Y91eonH/quZmpHHZ/HF8cNEE\n5oxTb5yISCrRnDeRYcrMOG1GCafNKKGyoZW7X97BPa/sYM/+NpraO/nj8m38cfk2Ksrz+eCiCVwy\nbxwF2SG/yxYRkSRTz5tICumMRPnn+n38+eXtPLOxivh/fdPTApxXMZYrFo7n1OklBLVSVURkSFLP\nm8gIkhYMcG7FWM6tGMuu+laWrtjJfa/uYGddK+HOKH9btZu/rdrNmPwMLp0/jisWjGfGmDy/yxYR\nkQGknjeRFBeNOl7cXMM9r+zg8bV7CHdGD/n8uHEFXDZ/HBfNK6c0L8OnKkVEpIt63kRGuEDAOHV6\nCadOL6GhtYNHV1fyl1d3smJbHQCrdzWwelcD//boOk6bXsJl88dxbsUYstP1r7+ISCrSf3uLDCMF\nWSGuOnEiV504kW01zdz/2i7uX7mTHbWtRKKOZzZW8czGKrLTg7xn9hgunFvOGTNLyEgL+l26iIj0\nkoZNRYY55xyvba/jryt38fAbldS3dBzyeV5mGufOHsuF88o4bXoJoWDAp0pFREYG7fOGwptIb4U7\nozy7sYq/vr6Lf67bR2tH5JDPC7JCnDt7DOcfV8Yp04vVIycikgSa8yYivZaeFuCc2WM4Z/YYWsKd\n/GPdPh5+YzdPbagi3BmlobWD+17dyX2v7iQvI41zZo/hvDljOWNGKVnpCnIiIkOBwpvICJWdnsZF\n88q5aF45jW0dPLluL4+u3sMzG70g19jeyV9X7uKvK3eRFQpyxswS3lsxlrNnjdFmwCIiPko4vJlZ\nBvBd4CPAKOAN4NvOuf89yn3fAW7s4aN251xmovWISOLyMkNcNn88l80fT1N7J/9cv4/HVlfy1IZ9\ntHVEae2I8MTavTyxdi9pAWPx1CLOnjWGc44dw8TibL/LFxEZUfrT83YHcAXwM+AtYAnwqJm9yzn3\nfC/u/yzQFPc+crgLRWTw5GakcfG8ci6eV05LuJNnN1bz97V7eHLdXva3ddIZ9c5cfeHtGr778JvM\nHJPL2ceO4ZxjR3P8hFE62UFEJMkSCm9mdiLwIeBrzrkfx9r+AKwB/hM4pRdfs9Q5V53I74vI4MhO\nT+O8OWM5b85YOiJRXt5SyxNr9/Dkm3vZ3dAGwMa9TWzc28R/P72JwuwQZ84s5V3HjOaMmaUU5aT7\n/CcQERl+Eu15uwKvp+zXXQ3OuTYz+y3wAzOb4JzbcZTvMDPLBxrdcFjyKjLMhYKBA5sB33RxBesq\nG/nHur08uX4fq3bUA1Df0sGDr+/mwdd3YwbHTyjkzJmlnDmzlLnjC9UrJyIyABINb/OBjc65/d3a\nX449Hw8cLbxtBnKBZjN7APiqc27v0X7YzEYDpd2apx29ZBEZKGbG7PJ8Zpfn8/mzZ7CvsY2nN1Tx\n9IZ9PLexmsb2TpyDldvrWbm9np89+RaF2SFOn1HKGTNKOGNmKWPyNcVVRCQRiYa3MqCyh/autvIj\n3FsH3AK8CLQDpwOfA040sxN6CITdXUfPCx5ExCej8zL5wAkT+MAJE+iIRFmxtY6nN+zjqQ372LjX\nm9pa39LBQ6t289Cq3QDMGJ3LaTNKOH1GCYunFJOTocXvIiK9kdAmvWa2CdjgnDu/W/tUYBPwZefc\nz/rwfVcDfwK+6Zz796Nce7ietwe1Sa/I0FPZ0MpzG6t5ZmMVz71Vxf62zndcEwoa88YXcvK0Yk6e\nVsyCiaPIDGlfOREZnnw5YcHM1gB7nXNnd2ufDawFPuOc+1Ufv7MSWOucOyeBenTCgkgK6IxEWbWz\ngeffqub5t6tYub2ezug7/zsoPS3AgomFnDy1hMVTizh+QqHCnIgMG36dsFAJjOuhvSz2vDuB79wB\nFCVYj4ikgLRggIWTRrFw0ii+eM4Mmto7Wb6phuffrmb55hrW72kEvGO8lm+uZfnmWsALc/MnFHLS\n1GIWTyli/sRROvFBREasRMPb68C7zCy/2xy1xXGf95qZGTAZWJlgPSKSgnJjR3CdM3sMADVN7by0\npZYXN9Xw4uYa3t7nzZcLd0Z5aUstL23xwlxawJgzroBFk0exaHIRJ0wu0rYkIjJiJBrelgL/AnwK\n6NrnLQP4GPBS1zYhZjYRyHbOre+60cxKnXNV3b7vs3jz2B5PsB4RGQaKczM4/7gyzj/O68Svbmrn\n5S21LN9cw/LNNQcWP3RGHa/vqOf1HfXc9twWAKaW5rBw4ihOmDyKhZOKmFaag/f/C0VEhpeEwptz\n7iUzuw/4YWwBwdvAtXi9Z5+Iu/QPwJlA/H+DbjOze4DVQBtwGt6Gv68DfZonJyLDW0m3MFfT1M6K\nbXW8sqWWV7bWsmb3fiKxOXObq5rZXNXMfa/uBKAwO8TxEwqZP2EUCyYVMm9CIfmZOpNVRFJff9bm\nfxT4HoeebXqhc+7Zo9z3J7wTGN4PZALb8E5l+DfnXEs/6hGRYa44N4P3VozlvRVjAWgJd7Jyez0r\nttbx6vY6Vm6ro7HdW81a39IR23vO6+g3g2mlucwbX8jxEwqYN6GQWWPzSU8L+PbnERFJREKrTYca\nrTYVEYBI1LFxbyMrttWxcnsdr2+vZ3N182GvTw8GOLY8n3njC5gzroC54wuYXppLWlCBTkSSx6/V\npiIiQ04wYBxbls+xZfl85KRJANQ1h3l9Rz2vba/j9R31rNpRf2CvuXAkyqpYW5fMUICK8gLmlOdT\nMa6AivJ8ZozOUw+diAwZCm8iMqyNyknnXbNG865ZowFwzrG1psULbTu94PZm5X7aOqIAtHVEeXVb\nHa9uqzvwHenBAMeMzWN2WT7HluUxu7yAWWV5mkMnIr5QeBOREcXMmFKSw5SSHC6d721X2RmJsqmq\nmTd21rN6VwOrdzWwLi7QhSPRA+3xJhRlMWtsPseOzeOYsfnMKstjcnEOwYBWuYpI8ii8iciIlxbr\nWTtmbB5XnjAB8ALdlupm1uxuYM2u/ayJBbr447121Layo7aV/31z74G2jLQAM8bkMnNMHjPH5HHM\nmDxmjs2jvCBTW5eIyIBQeBMR6UFaMMCMMXnMGJPHZfO9Nuccu+pbeXP3ftZVNvJmZQMb9jSyrbaF\nrrVf7Z3RWNjbf8j35aQHmT46l2mjc5kxOo/po3OZPjqXCaOytEBCRPpE4U1EpJfMjPGjshk/Kptz\nY9uVgLdlyca9Tayv3M/6PY1s3Os9qpvCB65pDkdYtbOBVTsPHXpNDwaYXJLNtFIvzE0tzWFqSS5T\nSnM0p05EeqTwJiLST9npaRw/oZDjJxQe0l7T1M7GvU28ta+Rt2LPb+9rprqp/cA14UiUjXubDpwe\nEa8kN4Opsfl5k0tymFycHXvO0dmuIiOYwpuISJIU52Zwcm4GJ08rPqS9viXMW/ua2LSviU1VTWyq\namZTVRM7aluIxm29Wd3U7h0RtrX2Hd89Jj+DSUU5TCzOZnJxNhOLc5hUlM2EomxGZYc0v05kGFN4\nExEZZIXZ6SyaXMSiyUWHtLd1RNhW08KW6iY2V3vHfW2p9h61zeFDrt27v529+3sOdrkZaYwflcWE\nomwmjMpmQlFWbLg3i3GjsjQcK5LiFN5ERIaIzFDwwKrX7hpaO9hW4wW5rdUtbKtpZlut9xw/tw6g\nqb2T9XsaWb+nscffKcgKMa4wi/LCLMYVZnrPo7IoK8iivDCT0XmZ2u5EZAhTeBMRSQEFWSHmji9k\n7vjCd3zW1N7J9poWttc2e9uX1LWwo7aFHXWt7KxrObBfXZeG1g4aWjt4s3L/O74LvJMqxuZnUlaQ\nSVlhFmUFmYyJvR9bkMnY/ExK8zIIaZWsiC8U3kREUlxuRhqzy/OZXZ7/js+cc9Q0h9kZC3I761rZ\nUdvC7vpWdtW3squuleZw5JB7IlFvS5Rd9a0Qd9JEPDMozkmnNC+TMfkZjM7LYHReJqPzMyjNzWB0\nvve+NC+DzJAWV4gMJIU3EZFhzMwoyc2gJDfjHathwQt3+9s62VXXyu76ViobWtnd0EZlvfe8J/YI\nR6Ld7oPqpjDVTWHWVR65hryMNEryvFBXkpdOSW4GxTne6+KcDEpy0ynOzaAoJ538zDQtthA5CoU3\nEZERzMwoyApRkBXqsecOvIBX2xxmz34vyFU2tLGvsZ2qxjb27m9nX+y5pqn9kNWyXRrbO2ls72RL\ndfNR6wkFjVHZ6RTlpFOcm37gdddzYXaIUdne+8LsEIXZIXIzFPhkZFF4ExGRIzIzinMzKM7NoKK8\n4LDXRaKOmuZ29u1vp6rRC3XVTWGqGtupavLaqhu97U/ijxmL1xFx7GtsZ19je4+f9yQtEAug2SEK\nY0G0MNvrxSvICpGfFSI/M/aclea9zvRe52WGtDhDUo7Cm4iIDIhgwLx5b3mZR7023BmlprmdmqYw\nVU3t1DaFqW0OU9Mcpra5ndpm731dSwe1zWEaWjsO+12dUW9eX0237VR6Kyc9SF5miLzMtNgjRG5m\nGnkZaeRmpJGb6T3nxB65GUFy0g++z0kPkpORRlYoSEBBUAaBwpuIiAy69LQAZQXe9iS90RmJUtfS\nQX1LmPrWDuqaw9S3dFAXe9/Q2kFDSwf1rV57Q2sH+1s7aGzvPHDu7OE0hyM0hyPs6XnxbZ9kpwfJ\nTk+LPQcPvM+Kvc4KBcmKPWenB8mMe58V8t5nhAIHXnuPAJlpXntGWlA9haLwJiIiQ19aMEBpXgal\neRl9ui8adTS2d7I/FvAa2zrZ3xZ7bu048Lqx7dDPmto7aYo9t3RbjXskLeFIn65PRChoZKR5oS4j\nLUhGWoD0tAAZoSAZwQAZoQDpseeMtCChoJGeFiA9GCSUZmQEvetD8c9xr0NBiz17r9O6ngMB0tO8\n57SgkR4MkBb0XodibWkB0/zDQaDwJiIiw1YgcHBBxoQEvyMSdTS1d9Ice3ivIzS1d9La0UlTe4SW\n9k6vBy8W9lrDnQeCXEvsdWtHhNaw92jpiBDpaXVHL3REHB2RTpp6Py1wUAUDRjBghLqegwGCAS/Y\nBWNBL9D1Pu750EeAoHFIW8Bir80IxD8HOKQtGPBeBwwCZnEPYu3e62AsaHZ/HTAjIy3A5QvG+/2P\n8rAU3kRERI4gGBcAB4pzjnAkSltHlLauUNfhPdo6IrR3tXdEaOuI0t4Zob3Ta+t6DndGae96dERo\n64wS7jzYHv8cjkTp6IzSHvHeJ1Mk6ohEHYnNQBwaCrJCCm8iIiJykJnFhjyDAxoKe8M5F+u9i9IR\nORjuwp1ROqOOcGc09tnBazojjs5olHDE0Rl7H45EvddR7/u6XndGY88RL8R1RqNE4q6JOIhEo3Gf\nO6Iudr2La4uFwKiLex/7/OBriL6jzbvHOYjEnvtqqE8rVHgTEREZQcyM9DRvHtxI4Jwj6rweQYcj\nGgt8UdfttTt4rSOxIe3BovAmIiIiw5aZHZg/N1yMjNgtIiIiMkwovImIiIikEIU3ERERkRSi8CYi\nIiKSQhTeRERERFKIwpuIiIhICkk4vJlZhpn9h5ntNrNWM3vJzN7Ty3vHmdm9ZlZvZvvN7EEzm5po\nLSIiIiIjRX963u4AvgL8CfgiEAEeNbPTjnSTmeUCTwFnAj8AbgTmA8+YWXE/6pH/3969x15d13Ec\nf75EBQ0pzQsXMTYG6cwGphDWGuatFpu40DQzNRXLudKpXbBsZs1pNg1Jm0kXc15Q2dS8tGlDZjrQ\nkhC8IKYDBISiQEHA9N0fn8+ZXw/n/C7H37n9zuuxfffl9/l+Pvw+57x3zuf9+3w/3+/XzMzM+r2a\nbtIraQJwMnBJRFyTy24BlgBXA0d00fw8YAwwISKeym0fym0vAmbU0iczMzOzTlDrzNs00kzbTaWC\niNgKzAYmSRrZTdunSolbbvsC8ChwUo39MTMzM+sItSZv44FlEbGprHxh3o+r1EjSTsAngacrHF4I\njJa0R419MjMzM+v3an226TBgTYXyUtnwKu32Agb2oO2L1X6xpH2BfcqKR1ftqZmZmVk/Umvythuw\nrUL51sLxau2osW3JeaSLHHawfPnybpqamZmZNVchX9m1lva1Jm9vkWbQyg0qHK/WjhrbltwA3FVW\nNhmYNXXq1G6ampmZmbWMkcAzvW1Ua/K2BhhRoXxY3q+u0m4DadZtWIVj3bUFICLWAeuKZZJWASvz\ntr2r9h/AaOBe4Hjg5Tr9DquNY9OaHJfW5di0JseldfV1bHYlJW6P1dK41uRtEXCkpCFlFy1MLBzf\nQUS8K+lZ4LAKhycC/4yIN3rbmYjYCNzX23a9Ian0z5cjYmk9f5f1jmPTmhyX1uXYtCbHpXXVKTa9\nnnErqfVq07uBAcD0UoGkgcCZwIKIWJnLDpB0YIW2h0s6rND248Dn2fF0qJmZmZkV1DTzFhELJN0F\nXJmv/lwOnA6MAs4qVL2F9CQFFcpuAM4BHpB0DfA26UkNrwO/qKU/ZmZmZp2i1tOmAF8HrgBOA/YE\nFnpaFDkAAAepSURBVANTImJ+V40i4g1Jk4FrgR+SZv/mARdGxPoP0B8zMzOzfq/m5C0/UeGSvFWr\nM7lK+SrgxFp/d5OsBy7Pe2stjk1rclxal2PTmhyX1tVSsVFENLsPZmZmZtZDtV6wYGZmZmZN4OTN\nzMzMrI04eTMzMzNrI07ezMzMzNqIkzczMzOzNuLkzczMzKyNOHnrhqSBkq6StFrSW5IWSDqm2f3q\nJJIOlzRL0lJJmyWtkDRH0tgKdQ+S9LCkNyVtkPRHSfs0o9+dSNKlkkLSkgrHjpD0uKQtktZKmilp\ncDP62QkkHSrpvvw52CJpiaRvl9VxTBpM0hhJd0hald/3FyRdJmn3snqOTZ1IGizp8jxWbMjfWWdU\nqdujMUXSTpK+K+kVSVslLZZ0Sr1ewwd5wkKn+D0wDbgOeAk4A3hQ0pER8XgT+9VJvgd8hvTs28XA\nUOB84O+SPh0RSwAk7Q/MBzYCM4DBwMXAIZImRMT2ZnS+U+T3fwawucKxccCjwPOkx+HtT4rNGOCL\nDexmR5B0LHA/6cHXVwBvAqNJ73upjmPSYJJGAgtJ31GzgA3AJNLNXz8FHJ/rOTb1tTdwGbAC+Acw\nuVKlXo4pPwO+D/wGeIoUy9skRUTc0eevICK8VdmACUAAFxfKBpGe5fpEs/vXKRtwBLBrWdkYYCtw\na6HsBmALcECh7Ogcw+nNfh39fQPuIA0484AlZcceBFYDQwplZ+fYHNvsvvenDRgCrAXmAjt1Uc8x\naXxsZuT39+Cy8j/k8j0dm4bEYSAwNP/7sPy+nlGhXo/GFGAEsB2YVSgTKfFbCQzo69fg06Zdmwa8\nA9xUKoj0WLDZwKT8V5TVWUQ8EWWzZhHxErAUOKhQ/GXgTxGxolDvEWAZcFIj+tqpJH2O9Hm5oMKx\nIcAxpER7U+HQLaQZIcemb30V2A+4NCLelfQhSe/7rndMmmZI3r9eVr4GeBfY7tjUX0Rsi4i1Paja\n0zHleGAXUrJXqhfAjaRZ00l90e8iJ29dGw8sK/sAQZr2BhjX4P5YJkmkAepf+ecRwL7A0xWqLyTF\n0upA0gDgeuDmiHi2QpVDSEs03hebnJAvwrHpa0cDm4ARkl4kDfibJN0oaVCu45g0x7y8ny1pnKSR\nkr4CfAuYGRGbcWxaQi/HlPGk5SLPV6gHdYiZk7euDSP9RVSuVDa8gX2x9zuVNFV9Z/55WN5Xi9de\nkgY2omMd6JvAx4AfVTneXWz8OepbY0iD/73An0mzB78lxel3uY5j0gQR8TDpc3IMaT3iCtJyg+sj\n4sJczbFpDb0ZU4YBr+fZtvJ6UIeY+YKFru0GbKtQvrVw3BpM0oHAr4AnSWtF4L1YdBevSsetRpI+\nCvwEuCIi1lep1l1s/DnqW4OB3YFfR0Tp6tK5knYFzpV0GY5JM71KWgt1D/Bv4EvADElrI2IWjk2r\n6M2Y0vBcwclb194iLWwsN6hw3BpI0lDgAdLVP9Mi4p18qBQLx6uxfkq6Yu76Lup0FxvHpW+V3s/b\ny8pvA84lrb/ZkssckwaSdDJpDfXYiFiVi+fmNYlXSbodf15aRW/GlIbnCj5t2rU1vDd1WlQqW93A\nvnQ8SR8GHgI+AnwhIorvf2l6ulq8NkSEZ936kKQxwHRgJjBc0ihJo0hfWLvkn/ei+9j4c9S3Su9n\n+aL4dXm/J45Js5wHPFNI3EruI82WjsexaRW9GVPWAEPzWuzyelCHmDl569oiYGy++qdoYuG4NUBe\naH0/MBaYEhHPFY9HxGvAetJl3+Um4FjVwwjSd8hM4JXCNpEUp1dI91JaAvyPstjk03jjcGz62t/y\nfkRZeWndzXock2bZDxhQoXyXvN8Zx6Yl9HJMWURKvg8qq1e3XMHJW9fuJn3QppcK8gLFM4EFEbGy\nWR3rJPlqxjtJp3tOjIgnq1S9B5hSvIWLpKNIicRdde9o51kCnFBhW0paiH0CMDsiNgKPAF+TtEeh\n/Wmk9VmOTd+ak/dnlZWfTUoK5jkmTbMMGF/h6TCnkG4VstixaSk9HVPuBd4mzayW6ol0kdBrwBN9\n3THteHGEFUmaQxqEriXdnPd0UtZ9VETMb2bfOoWk64DvkGbe5pQfj4hbc72RpCu4/gv8kvRFdwmw\nCjjcp00bQ9I8YO+I+ESh7FDSF9hzpDU/+wMXAfMj4rhm9LM/kzQb+Abp8/IY6Q7yJwJXRsSMXMcx\nabB8P8S/kC5UmJX3U0hPTbg5Is7J9RybOpN0PmkJznDSrVrmksYPSFf/buzNmCLp6nzsJtITFqaS\nLkY5NSJu6/MX0Ow7Hbf6Rlq/83PSOe2tpPu2HNfsfnXSRro3UlTbyuoeTLo9wmbgP8CtwH7Nfg2d\ntFHhCQu5/LPAX0mLd9eRBq89mt3f/riRTsP9mHRl43bSo/0ucEyav5H++H8wjynbgRdJT17Y2bFp\naBxe7WJcGVWo16MxhXQm8wf5/91GOjNxar3675k3MzMzszbiNW9mZmZmbcTJm5mZmVkbcfJmZmZm\n1kacvJmZmZm1ESdvZmZmZm3EyZuZmZlZG3HyZmZmZtZGnLyZmZmZtREnb2ZmZmZtxMmbmZmZWRtx\n8mZmZmbWRpy8mZmZmbURJ29mZmZmbcTJm5mZmVkb+T+9Q73lNXtgHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e499e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,\n",
    "      weight_decay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After executing the above code, a `submission.csv` file will be generated.  It is in the required format by Kaggle. Now, we can submit our predicted sales prices of houses based on the testing data set and compare them with the true prices on Kaggle. You need to log in Kaggle, open the [link to the house prediction problem]((https://www.kaggle.com/c/house-prices-advanced-regression-techniques)), and click the `Submit Predictions` button.\n",
    "\n",
    "\n",
    "![](../img/kaggle_submit.png)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "You may click the `Upload Submission File` button to select your results. Then click `Make Submission` at the bottom to view your results.\n",
    "\n",
    "![](../img/kaggle_submit2.png)\n",
    "\n",
    "Just a kind reminder again, **Kaggle limits the number of daily submissions to 10**.\n",
    "\n",
    "\n",
    "## Exercise ([Share your score and method here](https://discuss.mxnet.io/t/kaggle-exercise-1-house-price-prediction-with-gluon/51)):\n",
    "\n",
    "* What loss can you obtain on Kaggle by using this tutorial?\n",
    "* By re-designing and fine-tuning the model and $k$-fold cross-validation, can you beat [the 0.14765 baseline](https://www.kaggle.com/zubairahmed/randomforestregressor-with-score-of-0-14765/) achieved by Random Forest regressor (a powerful model) on Kaggle? You may start by getting some ideas after reading a few previous chapters, such as\n",
    " * [Multilayer perceptrons in gluon](../chapter03_deep-neural-networks/mlp-gluon.ipynb)\n",
    " * [Overfitting and regularization (with gluon)](../chapter02_supervised-learning/regularization-gluon.ipynb)\n",
    " * [Dropout regularization with gluon](../chapter03_deep-neural-networks/mlp-dropout-gluon.ipynb)"
   ]
  },
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   "source": [
    "For whinges or inquiries, [open an issue on  GitHub.](https://github.com/zackchase/mxnet-the-straight-dope)"
   ]
  }
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